From 4e68ceb52e9899d8d2bb0dc469622abc3f4e7475 Mon Sep 17 00:00:00 2001 From: samuel Date: Fri, 7 Aug 2026 22:59:28 +0200 Subject: [PATCH 01/70] update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b1b1c0f1..7d14730d 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ A battle-royal game with trained agent which controls tanks in realistic physic ## Description -Each agent receives the rendered frame of its camera as input, and it is trained to fire and hit enemies. +Each tank receives the rendered frame of its camera as input, and the agent is trained to fire and hit enemies. -When agent is trained (with SAC algorithm) you can fight against other tanks. +When the agent is trained (with the PPO or SAC algorithm), you can fight it through the tanks it handles. ## Installation From 38cae81225ff9a4b34fe446ccb8eca2e693eef67 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sat, 8 Aug 2026 15:40:18 +0200 Subject: [PATCH 02/70] * miss distance scale at 10.0 * increase by 5 times ppo continuous entropy coef --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 2 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index f7544732..146db195 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -32,7 +32,7 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - float continuous_entropy_coef = 0.0005f; + float continuous_entropy_coef = 0.0025f; float discrete_entropy_coef = 0.005f; int epochs = 4; int rollout_size = 30 * 30; diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 17b2f2e1..337bfcdf 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -34,7 +34,7 @@ namespace arenai::model { [this](const std::shared_ptr &shell) { on_shell_fired(shell); }, [this] { return nb_shells > 0; }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), - curr_frame_upside_down(0), distance_scale(250.f), miss_distance_scale(2.f), + curr_frame_upside_down(0), distance_scale(250.f), miss_distance_scale(10.f), hit_reward_scale(0.5f), optimal_distance(75.f), aim_angle_scale(glm::radians(10.f)), hit_received_cost(0.1f), fire_cost(0.05f), initial_nb_shells(30), nb_shells(initial_nb_shells), shells_recharged_per_hit(5), From 88d62d96bb8cbd20121b0985af7c250bab39291b Mon Sep 17 00:00:00 2001 From: samuel Date: Sat, 8 Aug 2026 16:41:50 +0200 Subject: [PATCH 03/70] constant target entropy for sac --- arenai_agent/src/agents/sac/sac_trainer.cpp | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index fa259620..ef15b7b6 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -51,10 +51,9 @@ namespace arenai::agent { group_norm_nums)), alpha_continuous(std::make_shared(0.01f)), alpha_discrete(std::make_shared(0.01f)), - continuous_target_entropy(std::make_shared( - nb_continuous_actions, 0.7f, 0.1f, 1000000)), - discrete_target_entropy( - std::make_shared(0.5f, 0.1f, 1000000)), + continuous_target_entropy( + std::make_shared(nb_continuous_actions, 0.1f)), + discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_1_optim(std::make_unique( From 44cbc9608c01f32971d008e22f52e54d3e361695 Mon Sep 17 00:00:00 2001 From: samuel Date: Sat, 8 Aug 2026 16:43:27 +0200 Subject: [PATCH 04/70] lower initt sigma --- arenai_agent/src/networks_utils/init.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/networks_utils/init.h b/arenai_agent/src/networks_utils/init.h index b3af2fd6..f470ba53 100644 --- a/arenai_agent/src/networks_utils/init.h +++ b/arenai_agent/src/networks_utils/init.h @@ -12,7 +12,7 @@ namespace arenai::agent { void init_hidden_weights(torch::nn::Module &module); void init_mu_output_weights(torch::nn::Module &module); - void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = 0.5f); + void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = 0.1f); void init_discrete_output_weights(torch::nn::Module &module); void init_value_output_weights(torch::nn::Module &module); From 58a2b6cbb75b338d75ac4a77ef84f39970213a44 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sat, 8 Aug 2026 19:00:38 +0200 Subject: [PATCH 05/70] higher entropy coef for ppo --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 146db195..794b63e4 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -32,8 +32,8 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - float continuous_entropy_coef = 0.0025f; - float discrete_entropy_coef = 0.005f; + float continuous_entropy_coef = 0.001f; + float discrete_entropy_coef = 0.01f; int epochs = 4; int rollout_size = 30 * 30; int minibatch_size = 1024; From b2932ee3914fe6da203be4879709b8bcfcb8d1e3 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 9 Aug 2026 02:03:04 +0200 Subject: [PATCH 06/70] * no fire cost -> collapse entropy ptherwise * lower continuous entropy coef for ppo --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 2 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 21 +++++++------------ arenai_model/src/tank/jolt_enemy_tank.h | 2 -- .../tests/src/tests_reward/tests_reward.cpp | 4 ++-- 4 files changed, 11 insertions(+), 18 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 794b63e4..5645f50a 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -32,7 +32,7 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - float continuous_entropy_coef = 0.001f; + float continuous_entropy_coef = 0.0005f; float discrete_entropy_coef = 0.01f; int epochs = 4; int rollout_size = 30 * 30; diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 337bfcdf..4f896709 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -36,10 +36,9 @@ namespace arenai::model { max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), curr_frame_upside_down(0), distance_scale(250.f), miss_distance_scale(10.f), hit_reward_scale(0.5f), optimal_distance(75.f), aim_angle_scale(glm::radians(10.f)), - hit_received_cost(0.1f), fire_cost(0.05f), initial_nb_shells(30), - nb_shells(initial_nb_shells), shells_recharged_per_hit(5), - is_dead_already_triggered(false), has_touch(false), has_fired(false), - fires_since_reward(0) {} + hit_received_cost(0.1f), initial_nb_shells(30), nb_shells(initial_nb_shells), + shells_recharged_per_hit(5), is_dead_already_triggered(false), has_touch(false), + has_fired(false) {} float JoltEnemyTank::compute_aim_angle(const std::shared_ptr &other_tank) { const auto canon_tr = get_canon()->get_model_matrix(); @@ -166,12 +165,10 @@ namespace arenai::model { // 2. dead / suicide penalty const auto dead_penalty = is_dead() ? -1.f : 0.f; - // 3. fired shells: pay a small cost at fire time (a shot toward nobody must be - // net-negative, break-even at gaussian ~0.1), then sample the closest tank along - // the trajectory and pay the dispersion gaussian (plus hit/kill bonuses) once the - // shell dies - float shells_reward = -fire_cost * static_cast(fires_since_reward); - fires_since_reward = 0; + // 3. fired shells: sample the closest tank along the trajectory and pay the + // dispersion gaussian (plus hit/kill bonuses) once the shell dies; firing itself + // is free — the limited shell reserve (recharged on hit) taxes the spam + float shells_reward = 0.f; for (int i = static_cast(tracked_shells.size()) - 1; i >= 0; i--) { auto &tracked = tracked_shells[i]; @@ -186,8 +183,7 @@ namespace arenai::model { if (tracked.has_sample) { // the gaussian stays an order of magnitude under the hit bonus: a gradient - // toward the aim, not a farmable income; fire_cost and the shell reserve - // tax the spam + // toward the aim, not a farmable income; the shell reserve taxes the spam shells_reward += hit_reward_scale * compute_hit_reward( @@ -211,7 +207,6 @@ namespace arenai::model { void JoltEnemyTank::on_shell_fired(const std::shared_ptr &shell) { nb_shells--; has_fired = true; - fires_since_reward++; tracked_shells.push_back( {.shell = shell, diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index c75a3c68..f64e97d0 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -74,7 +74,6 @@ namespace arenai::model { float aim_angle_scale; float hit_received_cost; - float fire_cost; int initial_nb_shells; int nb_shells; @@ -84,7 +83,6 @@ namespace arenai::model { bool has_touch; bool has_fired; - int fires_since_reward; std::vector tracked_shells; void on_shell_fired(const std::shared_ptr &shell); diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index 1a2f0404..0a9948f3 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -193,6 +193,6 @@ TEST_F(RewardTest, ZeroRewardWithEmptyTankList) { ASSERT_FALSE(std::isnan(reward)) << "reward should not be NaN with empty tank list"; ASSERT_FALSE(std::isinf(reward)) << "reward should not be Inf with empty tank list"; - // only the fire cost remains: no enemy to sample, so no gaussian income - ASSERT_FLOAT_EQ(reward, -0.05f); + // firing is free and there is no enemy to sample, so no reward at all + ASSERT_FLOAT_EQ(reward, 0.f); } From d2e884a1ccbfff6979fac981e469e8f2b3bea1b8 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 9 Aug 2026 02:40:34 +0200 Subject: [PATCH 07/70] fix sac : esperance of min and not min of esperances --- arenai_agent/src/agents/sac/sac_trainer.cpp | 30 +++++++----- arenai_agent/src/networks/q_function.cpp | 26 +++++----- arenai_agent/src/networks/q_function.h | 5 +- .../src/tests_networks/tests_q_function.cpp | 9 ++-- .../tests_q_function_consistency.cpp | 48 ++++--------------- 5 files changed, 46 insertions(+), 72 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index ef15b7b6..60f6921a 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -111,14 +111,17 @@ namespace arenai::agent { const auto next_discrete_entropy = multinomial_entropy(next_discrete_proba); - const auto next_target_q_value_1 = target_critic_1->value_expectation( - next_state.vision, next_state.proprioception, next_continuous_action, - next_discrete_proba); - const auto next_target_q_value_2 = target_critic_2->value_expectation( - next_state.vision, next_state.proprioception, next_continuous_action, - next_discrete_proba); - - const auto target_v_value = torch::min(next_target_q_value_1, next_target_q_value_2) + const auto next_target_q_values_1 = target_critic_1->value_per_discrete_action( + next_state.vision, next_state.proprioception, next_continuous_action); + const auto next_target_q_values_2 = target_critic_2->value_per_discrete_action( + next_state.vision, next_state.proprioception, next_continuous_action); + + const auto next_min_q_value = + (next_discrete_proba + * torch::min(next_target_q_values_1, next_target_q_values_2)) + .sum(-1, true); + + const auto target_v_value = next_min_q_value + alpha_continuous->alpha() * next_continuous_entropy + alpha_discrete->alpha() * next_discrete_entropy; @@ -163,11 +166,12 @@ namespace arenai::agent { const auto curr_discrete_entropy = multinomial_entropy(curr_discrete_proba); - const auto curr_q_value_1 = critic_1->value_expectation( - state.vision, state.proprioception, curr_continuous_action, curr_discrete_proba); - const auto curr_q_value_2 = critic_2->value_expectation( - state.vision, state.proprioception, curr_continuous_action, curr_discrete_proba); - const auto q_value = torch::min(curr_q_value_1, curr_q_value_2); + const auto curr_q_values_1 = critic_1->value_per_discrete_action( + state.vision, state.proprioception, curr_continuous_action); + const auto curr_q_values_2 = critic_2->value_per_discrete_action( + state.vision, state.proprioception, curr_continuous_action); + const auto q_value = + (curr_discrete_proba * torch::min(curr_q_values_1, curr_q_values_2)).sum(-1, true); const auto actor_loss = -torch::mean( alpha_continuous->alpha().detach() * curr_continuous_entropy diff --git a/arenai_agent/src/networks/q_function.cpp b/arenai_agent/src/networks/q_function.cpp index 9680021a..dcd91ee9 100644 --- a/arenai_agent/src/networks/q_function.cpp +++ b/arenai_agent/src/networks/q_function.cpp @@ -18,9 +18,11 @@ namespace arenai::agent { const std::vector &hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums) - : vision_encoder(register_module( - "vision_encoder", std::make_shared( - vision_height, vision_width, vision_channels, group_norm_nums))), + : nb_discrete_actions(nb_discrete_actions), + vision_encoder(register_module( + "vision_encoder", + std::make_shared( + vision_height, vision_width, vision_channels, group_norm_nums))), sensors_encoder(register_module( "sensors_encoder", torch::nn::Sequential( @@ -79,28 +81,26 @@ namespace arenai::agent { return to_value->forward(head->forward(encoded_hidden)); } - torch::Tensor QFunction::value_expectation( + torch::Tensor QFunction::value_per_discrete_action( const torch::Tensor &vision, const torch::Tensor &sensors, - const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions_proba) { + const torch::Tensor &continuous_actions) { const auto batch_size = vision.size(0); - const auto nb_discrete_actions = discrete_actions_proba.size(1); const auto common_encoded = encode_common(vision, sensors, continuous_actions); - const auto one_hots = torch::eye(nb_discrete_actions, discrete_actions_proba.options()); + const auto one_hots = torch::eye(nb_discrete_actions, common_encoded.options()); - auto result = torch::zeros({batch_size, 1}, common_encoded.options()); + std::vector q_values; + q_values.reserve(nb_discrete_actions); for (int a = 0; a < nb_discrete_actions; a++) { const auto discrete_encoded = discrete_action_encoder->forward(one_hots[a].unsqueeze(0).expand({batch_size, -1})); - const auto q_a = - to_value->forward(head->forward(torch::cat({common_encoded, discrete_encoded}, 1))); - - result = result + discrete_actions_proba.select(1, a).unsqueeze(1) * q_a; + q_values.push_back(to_value->forward( + head->forward(torch::cat({common_encoded, discrete_encoded}, 1)))); } - return result; + return torch::cat(q_values, 1); } torch::Tensor QFunction::encode_common( diff --git a/arenai_agent/src/networks/q_function.h b/arenai_agent/src/networks/q_function.h index a5bf1336..fc7ac36a 100644 --- a/arenai_agent/src/networks/q_function.h +++ b/arenai_agent/src/networks/q_function.h @@ -26,11 +26,12 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &sensors, const torch::Tensor &continuous_actions, const torch::Tensor &discrete_action_ohe); - torch::Tensor value_expectation( + torch::Tensor value_per_discrete_action( const torch::Tensor &vision, const torch::Tensor &sensors, - const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions_proba); + const torch::Tensor &continuous_actions); private: + int nb_discrete_actions; std::shared_ptr vision_encoder; torch::nn::Sequential sensors_encoder; torch::nn::Sequential continuous_action_encoder; diff --git a/arenai_agent/tests/src/tests_networks/tests_q_function.cpp b/arenai_agent/tests/src/tests_networks/tests_q_function.cpp index 329b6b8d..27c726bd 100644 --- a/arenai_agent/tests/src/tests_networks/tests_q_function.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_q_function.cpp @@ -9,7 +9,7 @@ using namespace arenai; using namespace arenai::agent; -TEST_P(QFunctionTestParam, TestQFunctionExpectation) { +TEST_P(QFunctionTestParam, TestQFunctionPerDiscreteAction) { const auto [layers, cont_actions_nb, discrete_actions_nb, sensors_nb, sensors_hidden_size, actions_hidden_size, batch_size] = GetParam(); @@ -28,15 +28,12 @@ TEST_P(QFunctionTestParam, TestQFunctionExpectation) { const auto sensors = torch::randn({batch_size, sensors_nb}); const auto continuous_actions = torch::rand({batch_size, cont_actions_nb}) * 2.f - 1.f; - const auto discrete_actions = - torch::softmax(torch::randn({batch_size, discrete_actions_nb}), -1); - const auto value = - q_function.value_expectation(image, sensors, continuous_actions, discrete_actions); + const auto value = q_function.value_per_discrete_action(image, sensors, continuous_actions); ASSERT_EQ(value.ndimension(), 2); ASSERT_EQ(value.size(0), batch_size); - ASSERT_EQ(value.size(1), 1); + ASSERT_EQ(value.size(1), discrete_actions_nb); } TEST_P(QFunctionTestParam, TestQFunctionOHE) { diff --git a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp index 4bb1f52f..5773ee41 100644 --- a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp @@ -11,10 +11,10 @@ using namespace arenai; using namespace arenai::agent; // ======================================================================== -// value_expectation must equal the weighted sum of value_ohe +// value_per_discrete_action must match value_ohe action by action // ======================================================================== -TEST_F(QFunctionConsistencyTest, ExpectationMatchesManualWeightedSum) { +TEST_F(QFunctionConsistencyTest, PerDiscreteActionMatchesOhe) { constexpr int height = 8, width = 8; constexpr int nb_sensors = 5, nb_cont = 3, nb_disc = 4; constexpr int batch = 4; @@ -24,50 +24,23 @@ TEST_F(QFunctionConsistencyTest, ExpectationMatchesManualWeightedSum) { const auto vision = torch::randint(0, 255, {batch, 3, height, width}, torch::kUInt8); const auto sensors = torch::randn({batch, nb_sensors}); const auto cont_actions = torch::randn({batch, nb_cont}); - const auto disc_proba = torch::softmax(torch::randn({batch, nb_disc}), -1); torch::NoGradGuard no_grad; - const auto v_exp = q.value_expectation(vision, sensors, cont_actions, disc_proba); + const auto q_per_action = q.value_per_discrete_action(vision, sensors, cont_actions); + + ASSERT_EQ(q_per_action.size(0), batch); + ASSERT_EQ(q_per_action.size(1), nb_disc); - auto v_manual = torch::zeros({batch, 1}); const auto one_hots = torch::eye(nb_disc); for (int a = 0; a < nb_disc; a++) { const auto ohe = one_hots[a].unsqueeze(0).expand({batch, -1}); const auto q_a = q.value_ohe(vision, sensors, cont_actions, ohe); - v_manual = v_manual + disc_proba.select(1, a).unsqueeze(1) * q_a; - } - - ASSERT_TRUE(torch::allclose(v_exp, v_manual, 1e-4, 1e-4)) - << "value_expectation should equal sum of proba[a] * value_ohe(one_hot[a])"; -} - -TEST_F(QFunctionConsistencyTest, ExpectationWithUniformProbaIsMeanOfOhe) { - constexpr int height = 8, width = 8; - constexpr int nb_sensors = 3, nb_cont = 2, nb_disc = 3; - constexpr int batch = 2; - - QFunction q(height, width, nb_sensors, nb_cont, nb_disc, 8, 8, {16}, {{3, 4}}, {2}); - - const auto vision = torch::randint(0, 255, {batch, 3, height, width}, torch::kUInt8); - const auto sensors = torch::randn({batch, nb_sensors}); - const auto cont_actions = torch::randn({batch, nb_cont}); - const auto uniform_proba = torch::ones({batch, nb_disc}) / static_cast(nb_disc); - - torch::NoGradGuard no_grad; - const auto v_exp = q.value_expectation(vision, sensors, cont_actions, uniform_proba); - - auto sum_q = torch::zeros({batch, 1}); - const auto one_hots = torch::eye(nb_disc); - for (int a = 0; a < nb_disc; a++) { - const auto ohe = one_hots[a].unsqueeze(0).expand({batch, -1}); - sum_q = sum_q + q.value_ohe(vision, sensors, cont_actions, ohe); + ASSERT_TRUE(torch::allclose(q_per_action.select(1, a).unsqueeze(1), q_a, 1e-4, 1e-4)) + << "value_per_discrete_action column " << a << " should equal value_ohe(one_hot[" << a + << "])"; } - const auto mean_q = sum_q / static_cast(nb_disc); - - ASSERT_TRUE(torch::allclose(v_exp, mean_q, 1e-4, 1e-4)) - << "With uniform probabilities, expectation should be mean of Q values"; } TEST_F(QFunctionConsistencyTest, ValueOheOutputFinite) { @@ -102,9 +75,8 @@ TEST_F(QFunctionGradientTest, GradientFlowsThroughQFunction) { const auto vision = torch::randint(0, 255, {batch, 3, height, width}, torch::kUInt8); const auto sensors = torch::randn({batch, nb_sensors}); const auto cont_actions = torch::randn({batch, nb_cont}); - const auto disc_proba = torch::softmax(torch::randn({batch, nb_disc}), -1); - const auto value = q.value_expectation(vision, sensors, cont_actions, disc_proba); + const auto value = q.value_per_discrete_action(vision, sensors, cont_actions); const auto loss = value.sum(); loss.backward(); From 065010509341e9ae6bd7b94a6175d3361e19d568 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 10 Aug 2026 20:08:30 +0200 Subject: [PATCH 08/70] learned coef for ppo entropies --- arenai_agent/src/agents/ppo/ppo_factory.cpp | 13 ++- .../src/agents/ppo/ppo_hyperparams.cpp | 3 +- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 4 +- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 81 ++++++++++++++++--- arenai_agent/src/agents/ppo/ppo_trainer.h | 24 ++++-- .../tests/src/tests_agents/tests_ppo.cpp | 10 +-- .../src/tests_agents/tests_ppo_training.cpp | 9 +-- 7 files changed, 104 insertions(+), 40 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_factory.cpp b/arenai_agent/src/agents/ppo/ppo_factory.cpp index 173ede37..8ab554af 100644 --- a/arenai_agent/src/agents/ppo/ppo_factory.cpp +++ b/arenai_agent/src/agents/ppo/ppo_factory.cpp @@ -21,13 +21,12 @@ namespace arenai::agent { collector(std::make_shared(rollout_buffer)), agent(std::make_shared(actor, device, collector)), trainer(std::make_shared( - actor, rollout_buffer, vision_height, vision_width, nb_sensors, - params.actor_learning_rate, params.critic_learning_rate, params.hidden_size_sensors, - params.critic_hidden_sizes, params.vision_channels, params.group_norm_nums, device, - params.metric_window_size, params.gamma, params.gae_lambda, params.clip_epsilon, - params.target_kl, params.grad_norm_max, params.continuous_entropy_coef, - params.discrete_entropy_coef, params.epochs, params.rollout_size, - params.minibatch_size)) {} + actor, rollout_buffer, vision_height, vision_width, nb_sensors, nb_continuous_actions, + params.actor_learning_rate, params.critic_learning_rate, params.alpha_learning_rate, + params.hidden_size_sensors, params.critic_hidden_sizes, params.vision_channels, + params.group_norm_nums, device, params.metric_window_size, params.gamma, + params.gae_lambda, params.clip_epsilon, params.target_kl, params.grad_norm_max, + params.epochs, params.rollout_size, params.minibatch_size)) {} std::shared_ptr PpoTorchAgentFactory::get_agent() { return agent; } diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp index 12d2acc9..f8d9d6a1 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp @@ -21,8 +21,7 @@ namespace arenai::agent { {"--clip_epsilon", &PpoHyperParams::clip_epsilon}, {"--target_kl", &PpoHyperParams::target_kl}, {"--grad_norm_max", &PpoHyperParams::grad_norm_max}, - {"--continuous_entropy_coef", &PpoHyperParams::continuous_entropy_coef}, - {"--discrete_entropy_coef", &PpoHyperParams::discrete_entropy_coef}, + {"--alpha_learning_rate", &PpoHyperParams::alpha_learning_rate}, {"--epochs", &PpoHyperParams::epochs}, {"--rollout_size", &PpoHyperParams::rollout_size}, {"--minibatch_size", &PpoHyperParams::minibatch_size}, diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 5645f50a..689d82dc 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -32,8 +32,8 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - float continuous_entropy_coef = 0.0005f; - float discrete_entropy_coef = 0.01f; + // lr of the SAC-style adaptive entropy alphas (fixed targets: sigma 0.1, fire proba 0.2) + float alpha_learning_rate = 3e-4f; int epochs = 4; int rollout_size = 30 * 30; int minibatch_size = 1024; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index caa3145c..83f9c0ce 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -32,34 +32,44 @@ namespace arenai::agent { PpoTrainer::PpoTrainer( std::shared_ptr actor, std::shared_ptr rollout_buffer, const int vision_height, const int vision_width, const int nb_sensors, - const float actor_learning_rate, const float critic_learning_rate, + const int nb_continuous_actions, const float actor_learning_rate, + const float critic_learning_rate, const float alpha_learning_rate, const int hidden_size_sensors, const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, const torch::Device device, const int metric_window_size, const float gamma, const float gae_lambda, const float clip_epsilon, const float target_kl, const float grad_norm_max, - const float continuous_entropy_coef, const float discrete_entropy_coef, const int epochs, - const int rollout_size, const int minibatch_size) + const int epochs, const int rollout_size, const int minibatch_size) : actor(std::move(actor)), rollout_buffer(std::move(rollout_buffer)), critic(std::make_shared( vision_height, vision_width, nb_sensors, hidden_size_sensors, critic_hidden_sizes, vision_channels, group_norm_nums)), + alpha_continuous(std::make_shared(0.01f)), + alpha_discrete(std::make_shared(0.01f)), + continuous_target_entropy( + std::make_shared(nb_continuous_actions, 0.1f)), + discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_optim(std::make_unique( critic->parameters(), torch::optim::AdamOptions(critic_learning_rate))), + alpha_continuous_optim(std::make_unique( + alpha_continuous->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), + alpha_discrete_optim(std::make_unique( + alpha_discrete->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), actor_mean_loss_metric(std::make_shared("π_μ", metric_window_size)), actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), critic_std_loss_metric(std::make_shared("v_σ", metric_window_size)), continuous_entropy_metric(std::make_shared("Hc", metric_window_size)), discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), + alpha_continuous_metric(std::make_shared("α_c", metric_window_size, 2, true)), + alpha_discrete_metric(std::make_shared("α_d", metric_window_size, 2, true)), sigma_metric(std::make_shared("σ", metric_window_size)), clip_fraction_metric(std::make_shared("clip", metric_window_size)), kl_metric(std::make_shared("kl", metric_window_size, 2, true)), gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), - grad_norm_max(grad_norm_max), continuous_entropy_coef(continuous_entropy_coef), - discrete_entropy_coef(discrete_entropy_coef), epochs(epochs), rollout_size(rollout_size), + grad_norm_max(grad_norm_max), epochs(epochs), rollout_size(rollout_size), minibatch_size(minibatch_size) { to(device); @@ -148,14 +158,32 @@ namespace arenai::agent { const auto discrete_entropy = multinomial_entropy(discrete_proba); const auto actor_loss = -torch::mean( - surrogate + continuous_entropy_coef * continuous_entropy - + discrete_entropy_coef * discrete_entropy); + surrogate + alpha_continuous->alpha().detach() * continuous_entropy + + alpha_discrete->alpha().detach() * discrete_entropy); actor_optim->zero_grad(); actor_loss.backward(); torch::nn::utils::clip_grad_norm_(actor->parameters(), grad_norm_max); actor_optim->step(); + // dual ascent: entropy above target -> alpha decays toward 0 (stops pushing + // sigma up); entropy below target -> alpha grows (prevents entropy collapse) + const auto alpha_continuous_loss = torch::mean( + alpha_continuous->log_alpha() + * (continuous_entropy.detach() - continuous_target_entropy->target_entropy())); + + alpha_continuous_optim->zero_grad(); + alpha_continuous_loss.backward(); + alpha_continuous_optim->step(); + + const auto alpha_discrete_loss = torch::mean( + alpha_discrete->log_alpha() + * (discrete_entropy.detach() - discrete_target_entropy->target_entropy())); + + alpha_discrete_optim->zero_grad(); + alpha_discrete_loss.backward(); + alpha_discrete_optim->step(); + // critic const auto values = critic->value(mb_vision, mb_proprioception); const auto critic_loss = torch::mse_loss(values, mb_returns, at::Reduction::Mean); @@ -174,6 +202,8 @@ namespace arenai::agent { continuous_entropy_metric->add(continuous_entropy.mean().item()); discrete_entropy_metric->add(discrete_entropy.mean().item()); + alpha_continuous_metric->add(alpha_continuous->alpha().item()); + alpha_discrete_metric->add(alpha_discrete->alpha().item()); sigma_metric->add(sigma.mean().item()); clip_fraction_metric->add( @@ -244,9 +274,18 @@ namespace arenai::agent { } std::vector> PpoTrainer::get_metrics() { - return {actor_mean_loss_metric, actor_std_loss_metric, critic_mean_loss_metric, - critic_std_loss_metric, continuous_entropy_metric, discrete_entropy_metric, - sigma_metric, clip_fraction_metric, kl_metric}; + return { + actor_mean_loss_metric, + actor_std_loss_metric, + critic_mean_loss_metric, + critic_std_loss_metric, + continuous_entropy_metric, + discrete_entropy_metric, + alpha_continuous_metric, + alpha_discrete_metric, + sigma_metric, + clip_fraction_metric, + kl_metric}; } void PpoTrainer::save(const std::filesystem::path &output_folder) { @@ -254,10 +293,16 @@ namespace arenai::agent { save_torch(output_folder, actor, "actor.pt"); save_torch(output_folder, critic, "critic.pt"); + save_torch(output_folder, alpha_continuous, "alpha_continuous.pt"); + save_torch(output_folder, alpha_discrete, "alpha_discrete.pt"); + // Optimizers save_torch(output_folder, actor_optim, "actor_optim.pt"); save_torch(output_folder, critic_optim, "critic_optim.pt"); + save_torch(output_folder, alpha_continuous_optim, "alpha_continuous_optim.pt"); + save_torch(output_folder, alpha_discrete_optim, "alpha_discrete_optim.pt"); + // string repr std::ostringstream actor_repr_oss; dump_module_tree(actor, actor_repr_oss, 0, "actor"); @@ -275,16 +320,30 @@ namespace arenai::agent { void PpoTrainer::set_train(const bool train) const { actor->train(train); critic->train(train); + + alpha_continuous->train(train); + alpha_discrete->train(train); + + continuous_target_entropy->train(train); + discrete_target_entropy->train(train); } void PpoTrainer::to(const torch::Device device) const { actor->to(device); critic->to(device); + + alpha_continuous->to(device); + alpha_discrete->to(device); + + continuous_target_entropy->to(device); + discrete_target_entropy->to(device); } int PpoTrainer::count_parameters() { return count_parameters_impl(actor->parameters()) - + count_parameters_impl(critic->parameters()); + + count_parameters_impl(critic->parameters()) + + count_parameters_impl(alpha_continuous->parameters()) + + count_parameters_impl(alpha_discrete->parameters()); } }// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index d37c81be..8153e687 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -6,6 +6,7 @@ #define ARENAI_PPO_TRAINER_H #include "../../networks/actor.h" +#include "../../networks/entropy.h" #include "../../networks/value_function.h" #include "../trainer.h" #include "./ppo_rollout_buffer.h" @@ -21,14 +22,13 @@ namespace arenai::agent { public: PpoTrainer( std::shared_ptr actor, std::shared_ptr rollout_buffer, - int vision_height, int vision_width, int nb_sensors, float actor_learning_rate, - float critic_learning_rate, int hidden_size_sensors, - const std::vector &critic_hidden_sizes, + int vision_height, int vision_width, int nb_sensors, int nb_continuous_actions, + float actor_learning_rate, float critic_learning_rate, float alpha_learning_rate, + int hidden_size_sensors, const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, torch::Device device, int metric_window_size, float gamma, float gae_lambda, float clip_epsilon, float target_kl, float grad_norm_max, - float continuous_entropy_coef, float discrete_entropy_coef, int epochs, - int rollout_size, int minibatch_size); + int epochs, int rollout_size, int minibatch_size); void step() override; @@ -44,8 +44,16 @@ namespace arenai::agent { std::shared_ptr critic; + // adaptive entropy coefficients (dual ascent toward fixed entropy targets) + std::shared_ptr alpha_continuous; + std::shared_ptr alpha_discrete; + std::shared_ptr continuous_target_entropy; + std::shared_ptr discrete_target_entropy; + std::shared_ptr actor_optim; std::shared_ptr critic_optim; + std::shared_ptr alpha_continuous_optim; + std::shared_ptr alpha_discrete_optim; std::shared_ptr actor_mean_loss_metric; std::shared_ptr actor_std_loss_metric; @@ -56,6 +64,9 @@ namespace arenai::agent { std::shared_ptr continuous_entropy_metric; std::shared_ptr discrete_entropy_metric; + std::shared_ptr alpha_continuous_metric; + std::shared_ptr alpha_discrete_metric; + // mean sigma of the truncated normal: direct view of the aim spread, Hc only bounds it std::shared_ptr sigma_metric; @@ -70,9 +81,6 @@ namespace arenai::agent { float grad_norm_max; - float continuous_entropy_coef; - float discrete_entropy_coef; - int epochs; // rollout horizon: one train() consumes rollout_size complete steps in a single update int rollout_size; diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index f6b9d06e..e24af445 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -32,8 +32,7 @@ std::unique_ptr PpoAgentTest::make_factory(const PpoTestCo .gae_lambda = 0.95f, .clip_epsilon = 0.2f, .grad_norm_max = 1.f, - .continuous_entropy_coef = 0.01f, - .discrete_entropy_coef = 0.01f, + .alpha_learning_rate = 1e-3f, .epochs = 1, .rollout_size = 8, .minibatch_size = 10}; @@ -66,7 +65,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 9); + ASSERT_EQ(metrics.size(), 11); } // ======================================================================== @@ -135,8 +134,9 @@ TEST_P(PpoSaveLoadParamTest, SaveCreatesExpectedFiles) { factory->get_trainer()->save(save_dir); const std::vector expected_files = { - "actor.pt", "critic.pt", "actor_optim.pt", - "critic_optim.pt", "actor_repr.txt", "critic_repr.txt", + "actor.pt", "critic.pt", "alpha_continuous.pt", "alpha_discrete.pt", + "actor_optim.pt", "critic_optim.pt", "alpha_continuous_optim.pt", "alpha_discrete_optim.pt", + "actor_repr.txt", "critic_repr.txt", }; for (const auto &f: expected_files) diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index 69fb8c02..29166ab0 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -21,8 +21,7 @@ PpoTrainingTest::make_factory(const PpoTrainingTestConfig &cfg) const { .gamma = 0.99f, .gae_lambda = 0.95f, .clip_epsilon = 0.2f, - .continuous_entropy_coef = 0.01f, - .discrete_entropy_coef = 0.01f, + .alpha_learning_rate = 1e-3f, .epochs = 2, .rollout_size = ROLLOUT_SIZE, .minibatch_size = MINIBATCH_SIZE}; @@ -77,9 +76,9 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { const auto agent = std::make_shared(actor, device, collector); // target_kl = 0 : early stop disabled so every minibatch applies its update const auto trainer = std::make_shared( - actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, 1e-3f, 1e-3f, 8, - std::vector{16}, vision_channels, group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, - 1.f, 0.01f, 0.01f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE); + actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, + cfg.nb_continuous_actions, 1e-3f, 1e-3f, 1e-3f, 8, std::vector{16}, vision_channels, + group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, 1.f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE); std::vector initial_parameters; for (const auto ¶meter: actor->parameters()) From 369626a271fb9c928bb6ce8a8c11d4762661cd48 Mon Sep 17 00:00:00 2001 From: samuel Date: Mon, 10 Aug 2026 20:19:49 +0200 Subject: [PATCH 09/70] normalize reward with std for SAC --- .../src/agents/sac/sac_replay_buffer.cpp | 34 ++++++++++-- .../src/agents/sac/sac_replay_buffer.h | 9 ++++ .../tests_replay_buffer_edge.cpp | 53 ++++++++++++++----- 3 files changed, 79 insertions(+), 17 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp index 6a70556a..d043fd01 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp @@ -5,6 +5,7 @@ #include "./sac_replay_buffer.h" #include +#include using namespace arenai; using namespace arenai::agent; @@ -13,7 +14,7 @@ namespace arenai::agent { SacReplayBuffer::SacReplayBuffer(const int memory_size) : initialized_(false), memory_size_(memory_size), nb_steps_(0), write_idx_(0), size_(0), - nb_tanks_(0) {} + nb_tanks_(0), reward_count_(0.), reward_mean_(0.), reward_m2_(0.) {} void SacReplayBuffer::initialize(const SacInputStep &first_step) { constexpr auto cpu = torch::kCPU; @@ -64,7 +65,9 @@ namespace arenai::agent { store_done_[idx].copy_(done_bool.logical_and(truncated_bool.logical_not())); // tanks already terminated before this step have no valid transition to store - store_sampleable_[idx].copy_(already_terminated_.logical_not()); + const auto valid_mask = already_terminated_.logical_not(); + store_sampleable_[idx].copy_(valid_mask); + update_reward_stats(step.reward, valid_mask); already_terminated_.logical_or_( done_bool.reshape({nb_tanks_}).logical_or(truncated_bool.reshape({nb_tanks_}))); @@ -91,6 +94,31 @@ namespace arenai::agent { advance_write_idx(); } + void SacReplayBuffer::update_reward_stats( + const torch::Tensor &rewards, const torch::Tensor &valid_mask) { + // Chan et al. parallel Welford update with the batch of valid tank rewards + const auto valid_rewards = + rewards.reshape({nb_tanks_}).to(torch::kFloat).masked_select(valid_mask); + const auto batch_count = static_cast(valid_rewards.numel()); + if (batch_count == 0.) return; + + const auto batch_mean = valid_rewards.mean().item(); + const auto batch_m2 = (valid_rewards - batch_mean).square().sum().item(); + + const double delta = batch_mean - reward_mean_; + const double total_count = reward_count_ + batch_count; + + reward_mean_ += delta * batch_count / total_count; + reward_m2_ += batch_m2 + delta * delta * reward_count_ * batch_count / total_count; + reward_count_ = total_count; + } + + float SacReplayBuffer::reward_scale() const { + if (reward_count_ < 2.) return 1.f; + const double std = std::sqrt(reward_m2_ / reward_count_); + return std > 1e-6 ? static_cast(std) : 1.f; + } + void SacReplayBuffer::advance_write_idx() { write_idx_ = (write_idx_ + 1) % nb_steps_; if (size_ < nb_steps_) size_++; @@ -126,7 +154,7 @@ namespace arenai::agent { .action = {.continuous_action = take(store_cont_action_, step_idx), .discrete_action = take(store_disc_action_, step_idx)}, - .reward = take(store_reward_, step_idx), + .reward = take(store_reward_, step_idx) / reward_scale(), .done = take(store_done_, step_idx), .next_state = { .vision = take(store_vision_, next_idx), diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.h b/arenai_agent/src/agents/sac/sac_replay_buffer.h index 9b1a57c4..346bfbb7 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.h +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.h @@ -45,6 +45,8 @@ namespace arenai::agent { private: void initialize(const SacInputStep &first_step); void advance_write_idx(); + void update_reward_stats(const torch::Tensor &rewards, const torch::Tensor &valid_mask); + float reward_scale() const; bool initialized_; @@ -68,6 +70,13 @@ namespace arenai::agent { torch::Tensor store_sampleable_; // [nb_tanks] tanks already done/truncated in the current episode torch::Tensor already_terminated_; + + // running reward statistics (Welford, over every reward ever added): rewards are + // divided by the running std at sample time, never mean-shifted (a shift would + // change the optimal policy) + double reward_count_; + double reward_mean_; + double reward_m2_; }; }// namespace arenai::agent diff --git a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp index c5dca4f6..2b8d2a1e 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp @@ -2,6 +2,8 @@ // Created by claude on 01/07/2026. // +#include + #include #include @@ -48,26 +50,49 @@ TEST_F(ReplayBufferEdgeTest, SampleBatchLargerThanSingleElement) { << "Batch size should be clamped to the single available transition"; } -TEST_F(ReplayBufferEdgeTest, RewardUnchangedAtSample) { +namespace { + SacInputStep create_step_with_reward(const float reward) { + SacInputStep step; + step.state.vision = torch::randint(255, {1, 3, 8, 8}, torch::kUInt8); + step.state.proprioception = torch::randn({1, 5}); + step.action.continuous_action = torch::randn({1, 3}); + step.action.discrete_action = torch::zeros({1, 2}); + step.action.discrete_action[0][0] = 1.0f; + step.reward = torch::full({1, 1}, reward); + step.done = torch::zeros({1, 1}); + step.truncated = torch::zeros({1, 1}); + return step; + } +}// namespace + +TEST_F(ReplayBufferEdgeTest, ConstantRewardsAreNotRescaled) { SacReplayBuffer buffer(10); - SacInputStep step; - step.state.vision = torch::randint(255, {1, 3, 8, 8}, torch::kUInt8); - step.state.proprioception = torch::randn({1, 5}); - step.action.continuous_action = torch::randn({1, 3}); - step.action.discrete_action = torch::zeros({1, 2}); - step.action.discrete_action[0][0] = 1.0f; - step.reward = torch::full({1, 1}, 2.0f); - step.done = torch::zeros({1, 1}); - step.truncated = torch::zeros({1, 1}); - - buffer.add(step); - buffer.add(step); + buffer.add(create_step_with_reward(2.0f)); + buffer.add(create_step_with_reward(2.0f)); const auto output = buffer.sample(1, torch::kCPU); ASSERT_NEAR(output.reward.item(), 2.0f, 1e-5f) - << "Base ReplayBuffer should return the stored reward unchanged at sample time"; + << "Zero reward variance must keep the scale at 1 (no division by ~0)"; +} + +TEST_F(ReplayBufferEdgeTest, RewardDividedByRunningStdAtSample) { + SacReplayBuffer buffer(10); + + // rewards {0, 4}: mean 2, population std 2 + buffer.add(create_step_with_reward(0.0f)); + buffer.add(create_step_with_reward(4.0f)); + buffer.finish_episode(create_random_state(8, 8, 5)); + + const auto output = buffer.sample(64, torch::kCPU); + + const auto rewards = output.reward.reshape({-1}); + for (int64_t i = 0; i < rewards.size(0); i++) { + const float r = rewards[i].item(); + ASSERT_TRUE(std::abs(r - 0.0f) < 1e-5f || std::abs(r - 2.0f) < 1e-5f) + << "Sampled reward " << r << " should be a stored reward divided by the running std"; + } } TEST_F(ReplayBufferEdgeTest, TruncatedStepIsNotStoredAsTerminal) { From 58fe08438b59c605d4a0369993985d6322bb5c82 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 10 Aug 2026 22:25:35 +0200 Subject: [PATCH 10/70] increase continuous target entropy (from sigma 0.1 to 0.3) --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 83f9c0ce..66568213 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -47,7 +47,7 @@ namespace arenai::agent { alpha_continuous(std::make_shared(0.01f)), alpha_discrete(std::make_shared(0.01f)), continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.1f)), + std::make_shared(nb_continuous_actions, 0.3f)), discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), From f1e98b34a1df719bfa91f5c329f0cc0f7c9830e1 Mon Sep 17 00:00:00 2001 From: samuel Date: Mon, 10 Aug 2026 22:26:22 +0200 Subject: [PATCH 11/70] increase contunous target entropy for sac (sigma 0.1 to 0.3) --- arenai_agent/src/agents/sac/sac_trainer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 60f6921a..d6f5004e 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -52,7 +52,7 @@ namespace arenai::agent { alpha_continuous(std::make_shared(0.01f)), alpha_discrete(std::make_shared(0.01f)), continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.1f)), + std::make_shared(nb_continuous_actions, 0.3f)), discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), From ec841d860a6855f7e0c535ce81562e58c19297c9 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 10 Aug 2026 22:42:52 +0200 Subject: [PATCH 12/70] remaining frames as a terminal step =x penalty like death --- arenai_agent/src/agents/ppo/ppo_collector.cpp | 6 ++---- arenai_agent/src/agents/ppo/ppo_collector.h | 4 +--- .../src/agents/ppo/ppo_rollout_buffer.cpp | 10 ++-------- .../src/agents/ppo/ppo_rollout_buffer.h | 4 +--- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 9 ++------- arenai_agent/src/agents/sac/sac_collector.cpp | 9 ++------- arenai_agent/src/agents/sac/sac_collector.h | 4 +--- .../src/agents/sac/sac_replay_buffer.cpp | 8 ++------ .../src/agents/sac/sac_replay_buffer.h | 4 +--- arenai_agent/src/agents/step_collector.h | 4 +--- arenai_agent/src/agents/torch_types.h | 1 - arenai_agent/src/core/train_environment.cpp | 14 +++++++------- arenai_agent/src/core/train_environment.h | 2 +- .../src/networks_utils/torch_converter.cpp | 11 +++-------- .../src/networks_utils/torch_converter.h | 3 +-- arenai_agent/src/train.cpp | 10 ++++------ .../tests_agents/tests_ppo_rollout_buffer.cpp | 3 +-- .../src/tests_agents/tests_ppo_training.cpp | 3 +-- .../src/tests_e2e/tests_e2e_agent_input.cpp | 2 +- .../tests_replay_buffer/create_random_step.cpp | 3 +-- .../tests_replay_buffer_edge.cpp | 18 +----------------- arenai_core/include/arenai_core/environment.h | 2 +- arenai_core/include/arenai_core/types.h | 1 - arenai_core/src/environment.cpp | 6 +++--- arenai_core/tests/src/tests_environment.cpp | 2 +- arenai_desktop/src/game.cpp | 3 +-- .../tests/src/tests_e2e_agent_input.cpp | 2 +- 27 files changed, 43 insertions(+), 105 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_collector.cpp b/arenai_agent/src/agents/ppo/ppo_collector.cpp index 8f87e0cc..b0e30543 100644 --- a/arenai_agent/src/agents/ppo/ppo_collector.cpp +++ b/arenai_agent/src/agents/ppo/ppo_collector.cpp @@ -21,16 +21,14 @@ namespace arenai::agent { last_discrete_log_prob = discrete_log_prob; } - void PpoStepCollector::on_transition( - const torch::Tensor &rewards, const torch::Tensor &done, const torch::Tensor &truncated) { + void PpoStepCollector::on_transition(const torch::Tensor &rewards, const torch::Tensor &done) { rollout_buffer->add( {.state = last_state, .action = last_action, .continuous_log_prob = last_continuous_log_prob, .discrete_log_prob = last_discrete_log_prob, .reward = rewards, - .done = done, - .truncated = truncated}); + .done = done}); } void PpoStepCollector::on_episode_end(const TorchState &final_state) { diff --git a/arenai_agent/src/agents/ppo/ppo_collector.h b/arenai_agent/src/agents/ppo/ppo_collector.h index 89c7a1c6..f98cb8a8 100644 --- a/arenai_agent/src/agents/ppo/ppo_collector.h +++ b/arenai_agent/src/agents/ppo/ppo_collector.h @@ -22,9 +22,7 @@ namespace arenai::agent { const TorchState &state, const TorchAction &action, const torch::Tensor &continuous_log_prob, const torch::Tensor &discrete_log_prob); - void on_transition( - const torch::Tensor &rewards, const torch::Tensor &done, - const torch::Tensor &truncated) override; + void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) override; void on_episode_end(const TorchState &final_state) override; diff --git a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp index 45aed232..cf382337 100644 --- a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.cpp @@ -27,11 +27,7 @@ namespace arenai::agent { // tanks already terminated before this step have no valid transition to store const auto valid = already_terminated_.logical_not(); already_terminated_.logical_or_( - step.done.detach() - .cpu() - .to(torch::kBool) - .reshape({nb_tanks}) - .logical_or(step.truncated.detach().cpu().to(torch::kBool).reshape({nb_tanks}))); + step.done.detach().cpu().to(torch::kBool).reshape({nb_tanks})); steps_.push_back( {.step = @@ -42,8 +38,7 @@ namespace arenai::agent { .continuous_log_prob = step.continuous_log_prob.detach().cpu(), .discrete_log_prob = step.discrete_log_prob.detach().cpu(), .reward = step.reward.detach().cpu(), - .done = step.done.detach().cpu(), - .truncated = step.truncated.detach().cpu()}, + .done = step.done.detach().cpu()}, .valid = valid}); // the freshly added step is pending: its closing observation is not known yet @@ -93,7 +88,6 @@ namespace arenai::agent { stack([](const StoredStep &s) { return s.step.discrete_log_prob; }), .rewards = stack([](const StoredStep &s) { return s.step.reward; }), .dones = stack([](const StoredStep &s) { return s.step.done; }), - .truncateds = stack([](const StoredStep &s) { return s.step.truncated; }), .bootstrap_state = bootstrap_state, .valids = stack([](const StoredStep &s) { return s.valid; }).unsqueeze(-1)}; diff --git a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h index 45aad4b3..924a5941 100644 --- a/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h +++ b/arenai_agent/src/agents/ppo/ppo_rollout_buffer.h @@ -21,7 +21,6 @@ namespace arenai::agent { torch::Tensor discrete_log_prob; torch::Tensor reward; torch::Tensor done; - torch::Tensor truncated; }; // On-policy rollout stacked on the time dimension: every tensor is [T, nb_tanks, ...] @@ -32,7 +31,6 @@ namespace arenai::agent { torch::Tensor discrete_log_probs; torch::Tensor rewards; torch::Tensor dones; - torch::Tensor truncateds; // [nb_tanks, ...] observation closing the last step, for the value bootstrap TorchState bootstrap_state; // [T, nb_tanks, 1] whether the (step, tank) pair is a live transition @@ -62,7 +60,7 @@ namespace arenai::agent { // observation closing the last stored step, set by finish_episode() std::optional final_state_; - // [nb_tanks] tanks already done/truncated in the current episode + // [nb_tanks] tanks already done in the current episode torch::Tensor already_terminated_; }; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 66568213..0b990679 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -246,19 +246,14 @@ namespace arenai::agent { const auto rewards = rollout.rewards.to(torch::kFloat); const auto dones = rollout.dones.to(torch::kFloat); - const auto truncateds = rollout.truncateds.to(torch::kFloat); const auto valids = rollout.valids.to(torch::kFloat); - // terminal: no bootstrap; truncated: bootstrap but stop the GAE recursion - const auto terminals = dones * (1.f - truncateds); - const auto boundaries = torch::max(dones, truncateds); - - const auto deltas = rewards + gamma * next_values * (1.f - terminals) - values; + const auto deltas = rewards + gamma * next_values * (1.f - dones) - values; auto advantages = torch::zeros_like(deltas); auto gae = torch::zeros({nb_tanks, 1}, deltas.options()); for (int64_t t = nb_steps - 1; t >= 0; t--) { - gae = deltas[t] + gamma * gae_lambda * (1.f - boundaries[t]) * gae; + gae = deltas[t] + gamma * gae_lambda * (1.f - dones[t]) * gae; advantages[t] = gae; } diff --git a/arenai_agent/src/agents/sac/sac_collector.cpp b/arenai_agent/src/agents/sac/sac_collector.cpp index c97c0e82..e8ef7c12 100644 --- a/arenai_agent/src/agents/sac/sac_collector.cpp +++ b/arenai_agent/src/agents/sac/sac_collector.cpp @@ -17,14 +17,9 @@ namespace arenai::agent { last_action = action; } - void SacStepCollector::on_transition( - const torch::Tensor &rewards, const torch::Tensor &done, const torch::Tensor &truncated) { + void SacStepCollector::on_transition(const torch::Tensor &rewards, const torch::Tensor &done) { replay_buffer->add( - {.state = last_state, - .action = last_action, - .reward = rewards, - .done = done, - .truncated = truncated}); + {.state = last_state, .action = last_action, .reward = rewards, .done = done}); } void SacStepCollector::on_episode_end(const TorchState &final_state) { diff --git a/arenai_agent/src/agents/sac/sac_collector.h b/arenai_agent/src/agents/sac/sac_collector.h index 209b9932..c93bd6e1 100644 --- a/arenai_agent/src/agents/sac/sac_collector.h +++ b/arenai_agent/src/agents/sac/sac_collector.h @@ -20,9 +20,7 @@ namespace arenai::agent { // concrete act-time channel, called by TrainableSacAgent::act void on_act(const TorchState &state, const TorchAction &action); - void on_transition( - const torch::Tensor &rewards, const torch::Tensor &done, - const torch::Tensor &truncated) override; + void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) override; void on_episode_end(const TorchState &final_state) override; diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp index d043fd01..58c4a949 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp @@ -53,23 +53,19 @@ namespace arenai::agent { const auto idx = static_cast(write_idx_); const auto done_bool = step.done.to(torch::kBool); - const auto truncated_bool = step.truncated.to(torch::kBool); store_vision_[idx].copy_(step.state.vision); store_proprioception_[idx].copy_(step.state.proprioception); store_cont_action_[idx].copy_(step.action.continuous_action.detach()); store_disc_action_[idx].copy_(step.action.discrete_action.detach()); store_reward_[idx].copy_(step.reward); - // a truncated step is not a real terminal: the critic must keep bootstrapping - // through it, only actual terminations (done && !truncated) cut the return - store_done_[idx].copy_(done_bool.logical_and(truncated_bool.logical_not())); + store_done_[idx].copy_(done_bool); // tanks already terminated before this step have no valid transition to store const auto valid_mask = already_terminated_.logical_not(); store_sampleable_[idx].copy_(valid_mask); update_reward_stats(step.reward, valid_mask); - already_terminated_.logical_or_( - done_bool.reshape({nb_tanks_}).logical_or(truncated_bool.reshape({nb_tanks_}))); + already_terminated_.logical_or_(done_bool.reshape({nb_tanks_})); advance_write_idx(); } diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.h b/arenai_agent/src/agents/sac/sac_replay_buffer.h index 346bfbb7..185cdbbd 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.h +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.h @@ -18,7 +18,6 @@ namespace arenai::agent { TorchAction action; torch::Tensor reward; torch::Tensor done; - torch::Tensor truncated; }; struct SacTrainStep { @@ -63,12 +62,11 @@ namespace arenai::agent { torch::Tensor store_cont_action_; torch::Tensor store_disc_action_; torch::Tensor store_reward_; - // real terminals only (done && !truncated): truncated steps stay bootstrappable torch::Tensor store_done_; // [mem, nb_tanks] whether the (step, tank) pair can start a sampled transition torch::Tensor store_sampleable_; - // [nb_tanks] tanks already done/truncated in the current episode + // [nb_tanks] tanks already done in the current episode torch::Tensor already_terminated_; // running reward statistics (Welford, over every reward ever added): rewards are diff --git a/arenai_agent/src/agents/step_collector.h b/arenai_agent/src/agents/step_collector.h index 118576fc..ac4913cb 100644 --- a/arenai_agent/src/agents/step_collector.h +++ b/arenai_agent/src/agents/step_collector.h @@ -18,9 +18,7 @@ namespace arenai::agent { public: virtual ~AbstractStepCollector() = default; - virtual void on_transition( - const torch::Tensor &rewards, const torch::Tensor &done, - const torch::Tensor &truncated) = 0; + virtual void on_transition(const torch::Tensor &rewards, const torch::Tensor &done) = 0; virtual void on_episode_end(const TorchState &final_state) = 0; }; diff --git a/arenai_agent/src/agents/torch_types.h b/arenai_agent/src/agents/torch_types.h index d18b902e..7b6dab83 100644 --- a/arenai_agent/src/agents/torch_types.h +++ b/arenai_agent/src/agents/torch_types.h @@ -22,7 +22,6 @@ namespace arenai::agent { TorchState states; torch::Tensor rewards; torch::Tensor is_done; - torch::Tensor is_truncated; }; }// namespace arenai::agent diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index 9b54abec..f4648878 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -37,7 +37,7 @@ namespace arenai::agent { hit_metric(std::make_shared("hit", 256, 2, true)), kill_metric(std::make_shared("kill", 16, 1)), nb_kills_episode(0) {} - std::vector> + std::vector> TrainTankEnvironment::step(const float time_delta, const std::vector &actions) { // tanks flagged done on a previous step already emitted their terminal transition: @@ -90,16 +90,16 @@ namespace arenai::agent { if (has_hit[i]) remaining_frames[i] += nb_frames_added_when_hit; - const auto &[state, reward, is_done, is_truncated] = step_result[i]; + const auto &[state, reward, is_done] = step_result[i]; if (is_done) { - step_result[i] = {state, reward, true, false}; if (!already_done[i] && !is_suicide[i]) nb_kills_episode++; done[i] = true; } + // starving out (no hit for too long) is a real death: penalized and terminal if (!done[i] && remaining_frames[i] <= 0) { - step_result[i] = {state, reward, true, true}; + step_result[i] = {state, reward - 1.f, true}; done[i] = true; } @@ -115,10 +115,10 @@ namespace arenai::agent { if (done[i]) continue; if (const long nb_not_done = std::ranges::count(done, false); nb_not_done == 1) { - const auto &[state, reward, is_done, is_truncated] = step_result[i]; + const auto &[state, reward, is_done] = step_result[i]; if (only_one_tank_alive()) - step_result[i] = {state, reward + 2.f, true, is_truncated}; // winner réel - else step_result[i] = {state, reward + 1.f, true, is_truncated};// timeout winner + step_result[i] = {state, reward + 2.f, true}; // winner réel + else step_result[i] = {state, reward + 1.f, true};// timeout winner done[i] = true; } diff --git a/arenai_agent/src/core/train_environment.h b/arenai_agent/src/core/train_environment.h index 51c091b0..bacbdc61 100644 --- a/arenai_agent/src/core/train_environment.h +++ b/arenai_agent/src/core/train_environment.h @@ -18,7 +18,7 @@ namespace arenai::agent { const std::filesystem::path &android_assets_path, float wanted_frequency, int max_episode_steps, int vision_height, int vision_width, int vision_num_threads); - std::vector> + std::vector> step(float time_delta, const std::vector &actions) override; std::vector get_phi_vector(); diff --git a/arenai_agent/src/networks_utils/torch_converter.cpp b/arenai_agent/src/networks_utils/torch_converter.cpp index 764f42c1..74e7061f 100644 --- a/arenai_agent/src/networks_utils/torch_converter.cpp +++ b/arenai_agent/src/networks_utils/torch_converter.cpp @@ -78,28 +78,23 @@ namespace arenai::agent { } TorchStep steps_to_tensor( - const std::vector> - &steps, + const std::vector> &steps, const int vision_height, const int vision_width) { std::vector states; std::vector rewards; std::vector are_done; - std::vector are_truncated; - for (const auto &[state, reward, is_done, is_truncated]: steps) { + for (const auto &[state, reward, is_done]: steps) { states.push_back(state); rewards.push_back(torch::tensor({reward}, torch::TensorOptions().dtype(torch::kFloat))); are_done.push_back( torch::tensor({is_done}, torch::TensorOptions().dtype(torch::kBool))); - are_truncated.push_back( - torch::tensor({is_truncated}, torch::TensorOptions().dtype(torch::kBool))); } return { .states = states_to_tensor(states, vision_height, vision_width), .rewards = torch::stack(rewards), - .is_done = torch::stack(are_done), - .is_truncated = torch::stack(are_truncated)}; + .is_done = torch::stack(are_done)}; } }// namespace arenai::agent diff --git a/arenai_agent/src/networks_utils/torch_converter.h b/arenai_agent/src/networks_utils/torch_converter.h index 53c93b03..3063e1dd 100644 --- a/arenai_agent/src/networks_utils/torch_converter.h +++ b/arenai_agent/src/networks_utils/torch_converter.h @@ -23,8 +23,7 @@ namespace arenai::agent { TorchState state_to_tensor(const core::State &state, int vision_height, int vision_width); TorchStep steps_to_tensor( - const std::vector> - &steps, + const std::vector> &steps, int vision_height, int vision_width); }// namespace arenai::agent diff --git a/arenai_agent/src/train.cpp b/arenai_agent/src/train.cpp index f3bcf7c2..05ed189b 100644 --- a/arenai_agent/src/train.cpp +++ b/arenai_agent/src/train.cpp @@ -129,12 +129,10 @@ namespace arenai::agent { const auto steps = env->step(environment_options.wanted_frequency, actions_for_env); const auto phi_tensor = torch::tensor(env->get_phi_vector()).unsqueeze(1); - const auto [torch_next_states, torch_rewards, torch_are_done, torch_are_truncated] = - steps_to_tensor( - steps, environment_options.vision_height, environment_options.vision_width); + const auto [torch_next_states, torch_rewards, torch_are_done] = steps_to_tensor( + steps, environment_options.vision_height, environment_options.vision_width); - const auto terminal_mask = torch::logical_not( - torch::logical_and(torch_are_done, torch::logical_not(torch_are_truncated))); + const auto terminal_mask = torch::logical_not(torch_are_done); const auto potential_reward = terminal_mask * train_options.potential_reward_gamma * phi_tensor - last_phi_tensor; @@ -142,7 +140,7 @@ namespace arenai::agent { const auto torch_final_reward = torch_rewards + potential_reward; // complete the pending transition - maybe train - collector->on_transition(torch_final_reward, torch_are_done, torch_are_truncated); + collector->on_transition(torch_final_reward, torch_are_done); trainer->step(); // step ending stuff diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp index 97f431e0..02bdbfcc 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp @@ -30,8 +30,7 @@ PpoInputStep PpoRolloutBufferTest::make_step(const TorchState &state, const torc .continuous_log_prob = torch::randn({NB_TANKS, 1}), .discrete_log_prob = torch::randn({NB_TANKS, 1}), .reward = torch::randn({NB_TANKS, 1}), - .done = done, - .truncated = torch::zeros({NB_TANKS, 1})}; + .done = done}; } PpoInputStep PpoRolloutBufferTest::make_step(const TorchState &state) { diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index 29166ab0..86c0361a 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -88,8 +88,7 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { // horizon so that the batch is complete when the trainer checks for (int t = 0; t < ROLLOUT_SIZE + 2; t++) { agent->act(make_state(cfg, nb_tanks)); - collector->on_transition( - torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); + collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); trainer->step(); } diff --git a/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp b/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp index cb569fdb..3675f2a5 100644 --- a/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp +++ b/arenai_agent/tests/src/tests_e2e/tests_e2e_agent_input.cpp @@ -51,7 +51,7 @@ namespace { const auto steps = env.step(FREQUENCY, actions); states.clear(); - for (const auto &[state, reward, done, truncated]: steps) states.push_back(state); + for (const auto &[state, reward, done]: steps) states.push_back(state); actions = agent.act(states, VISION_HEIGHT, VISION_WIDTH); } diff --git a/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp b/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp index 625dd93c..c1e22b73 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp @@ -22,6 +22,5 @@ arenai::agent::SacInputStep create_random_step( {torch::rand({1, nb_cont_actions}) * 2.f - 1.f, torch::softmax(torch::randn({1, nb_discrete_actions}), -1)}, torch::randn({1, 1}), - torch::full({1, 1}, done, torch::kBool), - torch::full({1, 1}, false, torch::kBool)}; + torch::full({1, 1}, done, torch::kBool)}; } diff --git a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp index 2b8d2a1e..fe3f728e 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp @@ -60,7 +60,6 @@ namespace { step.action.discrete_action[0][0] = 1.0f; step.reward = torch::full({1, 1}, reward); step.done = torch::zeros({1, 1}); - step.truncated = torch::zeros({1, 1}); return step; } }// namespace @@ -95,21 +94,6 @@ TEST_F(ReplayBufferEdgeTest, RewardDividedByRunningStdAtSample) { } } -TEST_F(ReplayBufferEdgeTest, TruncatedStepIsNotStoredAsTerminal) { - SacReplayBuffer buffer(10); - - auto step = create_random_step(8, 8, 3, 2, 5, true); - step.truncated = torch::full({1, 1}, true, torch::kBool); - - buffer.add(step); - buffer.finish_episode(create_random_state(8, 8, 5)); - - const auto output = buffer.sample(1, torch::kCPU); - - ASSERT_FALSE(output.done.to(torch::kBool).item()) - << "A truncated step must sample done=false so the critic keeps bootstrapping"; -} - TEST_F(ReplayBufferEdgeTest, DeadStepIsStoredAsTerminal) { SacReplayBuffer buffer(10); @@ -119,7 +103,7 @@ TEST_F(ReplayBufferEdgeTest, DeadStepIsStoredAsTerminal) { const auto output = buffer.sample(1, torch::kCPU); ASSERT_TRUE(output.done.to(torch::kBool).item()) - << "A real termination (done && !truncated) must sample done=true"; + << "A termination (done) must sample done=true"; } TEST_F(ReplayBufferEdgeTest, SampleWithZeroBatchSize) { diff --git a/arenai_core/include/arenai_core/environment.h b/arenai_core/include/arenai_core/environment.h index 2148cca4..8d30b5b3 100644 --- a/arenai_core/include/arenai_core/environment.h +++ b/arenai_core/include/arenai_core/environment.h @@ -29,7 +29,7 @@ namespace arenai::core { float wanted_frequency, int vision_height, int vision_width, int vision_num_threads, bool vision_thread_sleep); - virtual std::vector> + virtual std::vector> step(float time_delta, const std::vector &actions); std::vector reset(float spawn_width, float spawn_height); diff --git a/arenai_core/include/arenai_core/types.h b/arenai_core/include/arenai_core/types.h index dc5f9594..164057e3 100644 --- a/arenai_core/include/arenai_core/types.h +++ b/arenai_core/include/arenai_core/types.h @@ -19,7 +19,6 @@ namespace arenai::core { typedef float Reward; typedef bool IsDone; - typedef bool IsTruncated; typedef controller::user_input Action; }// namespace arenai::core diff --git a/arenai_core/src/environment.cpp b/arenai_core/src/environment.cpp index 76e0f11b..5427795d 100644 --- a/arenai_core/src/environment.cpp +++ b/arenai_core/src/environment.cpp @@ -30,7 +30,7 @@ namespace arenai::core { graphics_backend(graphics_backend), gl_context(graphics_backend->render_context()), rng(dev()), file_reader(file_reader) {} - std::vector> + std::vector> BaseTanksEnvironment::step(const float time_delta, const std::vector &actions) { // 1. apply action @@ -51,13 +51,13 @@ namespace arenai::core { vision_pool_->loop_wait(); // 4. build State - std::vector> result; + std::vector> result; result.reserve(tanks.size()); for (int i = 0; i < tanks.size(); i++) { result.emplace_back( State(vision_pool_->read_vision(i), tanks[i]->get_proprioception()), - tanks[i]->get_reward(tanks), tanks[i]->is_dead(), false); + tanks[i]->get_reward(tanks), tanks[i]->is_dead()); } return result; diff --git a/arenai_core/tests/src/tests_environment.cpp b/arenai_core/tests/src/tests_environment.cpp index 599e33fa..c5c8ca9b 100644 --- a/arenai_core/tests/src/tests_environment.cpp +++ b/arenai_core/tests/src/tests_environment.cpp @@ -213,7 +213,7 @@ TEST_F(EnvironmentTest, StepRewardAndDoneAreValid) { const std::vector actions(nb_tanks, {{0.f, 0.f}, {0.f, 0.f}, {false}}); for (const auto results = env.step(frequency, actions); - const auto &[state, reward, is_done, is_truncated]: results) { + const auto &[state, reward, is_done]: results) { ASSERT_FALSE(std::isnan(reward)) << "reward should not be NaN"; ASSERT_FALSE(std::isinf(reward)) << "reward should not be Inf"; } diff --git a/arenai_desktop/src/game.cpp b/arenai_desktop/src/game.cpp index 2fa6a8ab..4db64a7c 100644 --- a/arenai_desktop/src/game.cpp +++ b/arenai_desktop/src/game.cpp @@ -159,8 +159,7 @@ namespace arenai::desktop { states.clear(); - for (const auto &[state, reward, done, is_truncated]: steps) - states.push_back(state); + for (const auto &[state, reward, done]: steps) states.push_back(state); auto now = std::chrono::steady_clock::now(); auto dt = now - last_time; diff --git a/arenai_desktop/tests/src/tests_e2e_agent_input.cpp b/arenai_desktop/tests/src/tests_e2e_agent_input.cpp index c55d6242..08c7e475 100644 --- a/arenai_desktop/tests/src/tests_e2e_agent_input.cpp +++ b/arenai_desktop/tests/src/tests_e2e_agent_input.cpp @@ -53,7 +53,7 @@ namespace { const auto steps = env.step(FREQUENCY, actions); states.clear(); - for (const auto &[state, reward, done, truncated]: steps) states.push_back(state); + for (const auto &[state, reward, done]: steps) states.push_back(state); actions = agent.act(states, VISION_HEIGHT, VISION_WIDTH); } From 8bc0b9abb4237c0c0f03c1e331a1a9ad50f4ec9a Mon Sep 17 00:00:00 2001 From: samuel Date: Tue, 11 Aug 2026 00:44:17 +0200 Subject: [PATCH 13/70] torch check for uint8 at vision network : no runtime_error -> it is a torch error --- arenai_agent/src/networks/vision.cpp | 2 +- .../tests/src/tests_networks/tests_vision_edge.cpp | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/arenai_agent/src/networks/vision.cpp b/arenai_agent/src/networks/vision.cpp index edbec3c9..a51039fa 100644 --- a/arenai_agent/src/networks/vision.cpp +++ b/arenai_agent/src/networks/vision.cpp @@ -39,7 +39,7 @@ namespace arenai::agent { } torch::Tensor ConvolutionNetwork::forward(const torch::Tensor &input) { - if (input.dtype() != torch::kUInt8) throw std::runtime_error("Input must be UInt8"); + TORCH_CHECK(input.dtype() == torch::kUInt8, "Input must be UInt8"); return cnn->forward(input.to(torch::kFloat).mul_(2.0f / 255.0f).add_(-1.0f)); } diff --git a/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp b/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp index 87d17ff8..3cf4fc1c 100644 --- a/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp @@ -14,8 +14,7 @@ TEST_F(VisionEdgeTest, RejectsNonUint8Input) { const auto float_input = torch::randn({1, 3, 8, 8}); - ASSERT_THROW(conv.forward(float_input), std::runtime_error) - << "Should throw when input is not UInt8"; + ASSERT_THROW(conv.forward(float_input), c10::Error) << "Should throw when input is not UInt8"; } TEST_F(VisionEdgeTest, NormalizesToExpectedRange) { @@ -35,11 +34,13 @@ TEST_F(VisionEdgeTest, OutputSizeMatchesGetOutputSize) { const std::vector> channels = {{3, 8}, {8, 16}}; const std::vector gnums = {4, 4}; constexpr int h = 16, w = 16; + constexpr int batch_size = 2; ConvolutionNetwork conv(h, w, channels, gnums); - const auto input = torch::randint(255, {2, 3, h, w}, torch::kUInt8); + const auto input = torch::randint(255, {batch_size, 3, h, w}, torch::kUInt8); const auto output = conv.forward(input); + ASSERT_EQ(output.size(0), batch_size); ASSERT_EQ(output.size(1), conv.get_output_size()); } From 0e4649e9e2c314193c4556f66ca1983c258f64d5 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Tue, 11 Aug 2026 14:53:29 +0200 Subject: [PATCH 14/70] lower sigma for continuous target entropy (from 0.3 to 0.1) --- arenai_agent/src/main.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index ed72db5f..1ec04bf0 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -26,7 +26,7 @@ int main(const int argc, char **argv) { parser.add_argument("--max_episode_steps").scan<'i', int>().default_value(30 * 60 * 3); parser.add_argument("--potential_reward_gamma").scan<'g', float>().default_value(0.997f); parser.add_argument("--nb_episodes").scan<'i', int>().default_value(2000); - parser.add_argument("--save_every").scan<'i', int>().default_value(30 * 60 * 3 * 5); + parser.add_argument("--save_every").scan<'i', int>().default_value(30 * 60 * 3 * 20); parser.add_argument("--cuda").default_value(false).implicit_value(true); // env From 4b7764285f37dd2e9b74839652f738d93cc6d6b7 Mon Sep 17 00:00:00 2001 From: samuel Date: Tue, 11 Aug 2026 14:56:09 +0200 Subject: [PATCH 15/70] actor init sigma to 0.1 --- arenai_agent/src/networks/actor.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/networks/actor.cpp b/arenai_agent/src/networks/actor.cpp index d2ce840a..abe188ed 100644 --- a/arenai_agent/src/networks/actor.cpp +++ b/arenai_agent/src/networks/actor.cpp @@ -64,7 +64,7 @@ namespace arenai::agent { head->apply(init_hidden_weights); mu->apply(init_mu_output_weights); - sigma->apply([](torch::nn::Module &m) { init_sigma_output_weights(m, 0.5f); }); + sigma->apply([](torch::nn::Module &m) { init_sigma_output_weights(m, 0.1f); }); discrete->apply(init_discrete_output_weights); } From 2076ec7a1d77e203d8a45a6312dc005b3026672c Mon Sep 17 00:00:00 2001 From: samuel Date: Tue, 11 Aug 2026 14:56:39 +0200 Subject: [PATCH 16/70] actor init sigma to 0.1 --- arenai_agent/src/networks/actor.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/networks/actor.cpp b/arenai_agent/src/networks/actor.cpp index abe188ed..5cd6013f 100644 --- a/arenai_agent/src/networks/actor.cpp +++ b/arenai_agent/src/networks/actor.cpp @@ -64,7 +64,7 @@ namespace arenai::agent { head->apply(init_hidden_weights); mu->apply(init_mu_output_weights); - sigma->apply([](torch::nn::Module &m) { init_sigma_output_weights(m, 0.1f); }); + sigma->apply([](torch::nn::Module &m) { init_sigma_output_weights(m); }); discrete->apply(init_discrete_output_weights); } From f393fc2192fbe7edf31d6167f5ccafa3a47b2017 Mon Sep 17 00:00:00 2001 From: samuel Date: Tue, 11 Aug 2026 14:59:02 +0200 Subject: [PATCH 17/70] lower sigma for continuous target entropy of SAC (from 0.3 to 0.1) --- arenai_agent/src/agents/sac/sac_trainer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index d6f5004e..60f6921a 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -52,7 +52,7 @@ namespace arenai::agent { alpha_continuous(std::make_shared(0.01f)), alpha_discrete(std::make_shared(0.01f)), continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.3f)), + std::make_shared(nb_continuous_actions, 0.1f)), discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), From c1a18e57fd9ee1ec5227596c534899edd4bcdde9 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Tue, 11 Aug 2026 15:19:14 +0200 Subject: [PATCH 18/70] lower sigma for continuous target entropy (from 0.3 to 0.1) --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 0b990679..eb53a75a 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -47,7 +47,7 @@ namespace arenai::agent { alpha_continuous(std::make_shared(0.01f)), alpha_discrete(std::make_shared(0.01f)), continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.3f)), + std::make_shared(nb_continuous_actions, 0.1f)), discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), From c6dfa53fae4813b6c630c59f4477cf8ef9db5026 Mon Sep 17 00:00:00 2001 From: samuel Date: Wed, 12 Aug 2026 04:38:14 +0200 Subject: [PATCH 19/70] * fix discrete head init to be target fire probability * higher aim angle scale for potential reward * fix default agent algorithm at CLI * no curriculum learning : same spawn zone sizes during training --- arenai_agent/src/agents/agent_cli.cpp | 3 --- arenai_agent/src/agents/agent_cli.h | 3 +-- arenai_agent/src/agents/sac/sac_hyperparams.h | 2 +- arenai_agent/src/main.cpp | 18 ++++++++---------- arenai_agent/src/networks/actor.cpp | 2 +- arenai_agent/src/networks_utils/init.cpp | 13 +++++++++++-- arenai_agent/src/networks_utils/init.h | 3 ++- arenai_model/src/tank/jolt_enemy_tank.cpp | 2 +- 8 files changed, 25 insertions(+), 21 deletions(-) diff --git a/arenai_agent/src/agents/agent_cli.cpp b/arenai_agent/src/agents/agent_cli.cpp index 7da3a8e4..7e0d671b 100644 --- a/arenai_agent/src/agents/agent_cli.cpp +++ b/arenai_agent/src/agents/agent_cli.cpp @@ -47,7 +47,4 @@ namespace arenai::agent { return algorithms; } - AgentCli get_default_agent_cli() { - return make_agent_cli("sac", sac_cli_fields()); - } }// namespace arenai::agent diff --git a/arenai_agent/src/agents/agent_cli.h b/arenai_agent/src/agents/agent_cli.h index ea013838..5426a1ba 100644 --- a/arenai_agent/src/agents/agent_cli.h +++ b/arenai_agent/src/agents/agent_cli.h @@ -29,10 +29,9 @@ namespace arenai::agent { create_factory; }; + // the first entry is the algorithm used when no subcommand is given std::vector make_agent_clis(); - AgentCli get_default_agent_cli(); - }// namespace arenai::agent #endif//ARENAI_ALGORITHMS_H diff --git a/arenai_agent/src/agents/sac/sac_hyperparams.h b/arenai_agent/src/agents/sac/sac_hyperparams.h index dc5fb840..63218028 100644 --- a/arenai_agent/src/agents/sac/sac_hyperparams.h +++ b/arenai_agent/src/agents/sac/sac_hyperparams.h @@ -26,7 +26,7 @@ namespace arenai::agent { std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; int metric_window_size = 256; float tau = 0.005f; - float gamma = 0.995f; + float gamma = 0.997f; int replay_buffer_size = 300000; int train_every = 256; int epochs = 128; diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 1ec04bf0..49a8253d 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -35,10 +35,10 @@ int main(const int argc, char **argv) { parser.add_argument("--nb_tanks").scan<'i', int>().default_value(32); parser.add_argument("--vision_height").scan<'i', int>().default_value(128); parser.add_argument("--vision_width").scan<'i', int>().default_value(256); - parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(500.f); - parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(500.f); - parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(2000.f); - parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(2000.f); + parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(750.f); + parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(750.f); + parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(750.f); + parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(750.f); parser.add_argument("--vision_num_threads") .scan<'i', int>() .default_value(static_cast(std::thread::hardware_concurrency())); @@ -48,7 +48,9 @@ int main(const int argc, char **argv) { parser.parse_args(argc, argv); - const AgentCli *selected_algorithm = nullptr; + // first algorithm by default: its subparser is not parsed, argparse falls back on + // the defaults carried by its arguments + const AgentCli *selected_algorithm = &algorithms.front(); for (const auto &algorithm: algorithms) if (parser.is_subcommand_used(algorithm.name)) selected_algorithm = &algorithm; @@ -59,11 +61,7 @@ int main(const int argc, char **argv) { const int vision_height = parser.get("--vision_height"); const int vision_width = parser.get("--vision_width"); - const auto create_factory_function = selected_algorithm == nullptr - ? get_default_agent_cli().create_factory - : selected_algorithm->create_factory; - - const auto agent_factory = create_factory_function( + const auto agent_factory = selected_algorithm->create_factory( vision_height, vision_width, cuda ? torch::Device(torch::kCUDA) : torch::Device(torch::kCPU)); diff --git a/arenai_agent/src/networks/actor.cpp b/arenai_agent/src/networks/actor.cpp index 5cd6013f..03113aac 100644 --- a/arenai_agent/src/networks/actor.cpp +++ b/arenai_agent/src/networks/actor.cpp @@ -66,7 +66,7 @@ namespace arenai::agent { mu->apply(init_mu_output_weights); sigma->apply([](torch::nn::Module &m) { init_sigma_output_weights(m); }); - discrete->apply(init_discrete_output_weights); + discrete->apply([](torch::nn::Module &m) { init_discrete_output_weights(m); }); } ActorRawOutput Actor::act(const torch::Tensor &vision, const torch::Tensor &sensors) { diff --git a/arenai_agent/src/networks_utils/init.cpp b/arenai_agent/src/networks_utils/init.cpp index 25caef68..eb04e53f 100644 --- a/arenai_agent/src/networks_utils/init.cpp +++ b/arenai_agent/src/networks_utils/init.cpp @@ -53,10 +53,19 @@ namespace arenai::agent { } } - void init_discrete_output_weights(torch::nn::Module &module) { + void + init_discrete_output_weights(torch::nn::Module &module, const float initial_fire_probability) { if (auto *lin = module.as()) { torch::nn::init::orthogonal_(lin->weight, 0.01f); - if (lin->options.bias()) torch::nn::init::zeros_(lin->bias); + + if (lin->options.bias()) { + torch::nn::init::zeros_(lin->bias); + + lin->bias.data().index_fill_( + 0, torch::tensor({0}), std::log(initial_fire_probability)); + lin->bias.data().index_fill_( + 0, torch::tensor({1}), std::log(1.f - initial_fire_probability)); + } } } diff --git a/arenai_agent/src/networks_utils/init.h b/arenai_agent/src/networks_utils/init.h index f470ba53..3c48325e 100644 --- a/arenai_agent/src/networks_utils/init.h +++ b/arenai_agent/src/networks_utils/init.h @@ -13,7 +13,8 @@ namespace arenai::agent { void init_mu_output_weights(torch::nn::Module &module); void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = 0.1f); - void init_discrete_output_weights(torch::nn::Module &module); + void + init_discrete_output_weights(torch::nn::Module &module, float initial_fire_probability = 0.2f); void init_value_output_weights(torch::nn::Module &module); diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 4f896709..1889dde9 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -35,7 +35,7 @@ namespace arenai::model { [this] { return nb_shells > 0; }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), curr_frame_upside_down(0), distance_scale(250.f), miss_distance_scale(10.f), - hit_reward_scale(0.5f), optimal_distance(75.f), aim_angle_scale(glm::radians(10.f)), + hit_reward_scale(0.5f), optimal_distance(75.f), aim_angle_scale(glm::radians(30.f)), hit_received_cost(0.1f), initial_nb_shells(30), nb_shells(initial_nb_shells), shells_recharged_per_hit(5), is_dead_already_triggered(false), has_touch(false), has_fired(false) {} From c8b8057a07aee98aa18ad374a75282afef8edfe5 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 12 Aug 2026 04:41:22 +0200 Subject: [PATCH 20/70] designation --- arenai_agent/src/main.cpp | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 49a8253d..489c7277 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -66,14 +66,22 @@ int main(const int argc, char **argv) { cuda ? torch::Device(torch::kCUDA) : torch::Device(torch::kCPU)); train_main( - {parser.get("--wanted_frequency"), parser.get("--nb_tanks"), vision_height, - vision_width, parser.get("--initial_spawn_width"), - parser.get("--initial_spawn_height"), parser.get("--final_spawn_width"), - parser.get("--final_spawn_height"), parser.get("--vision_num_threads")}, - {std::filesystem::path(parser.get("--output_folder")), - std::filesystem::path(parser.get("--resources_folder")), - parser.get("--potential_reward_gamma"), parser.get("--max_episode_steps"), - parser.get("--nb_episodes"), parser.get("--save_every"), cuda}, + {.wanted_frequency = parser.get("--wanted_frequency"), + .nb_tanks = parser.get("--nb_tanks"), + .vision_height = vision_height, + .vision_width = vision_width, + .initial_spawn_width = parser.get("--initial_spawn_width"), + .initial_spawn_height = parser.get("--initial_spawn_height"), + .final_spawn_width = parser.get("--final_spawn_width"), + .final_spawn_height = parser.get("--final_spawn_height"), + .num_threads = parser.get("--vision_num_threads")}, + {.output_folder = std::filesystem::path(parser.get("--output_folder")), + .resources_folder = std::filesystem::path(parser.get("--resources_folder")), + .potential_reward_gamma = parser.get("--potential_reward_gamma"), + .max_episode_steps = parser.get("--max_episode_steps"), + .nb_episodes = parser.get("--nb_episodes"), + .save_every = parser.get("--save_every"), + .cuda = cuda}, agent_factory); return 0; From 1c5b32e6245c2b56743e136108b23d67b45ee836 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 12 Aug 2026 05:02:49 +0200 Subject: [PATCH 21/70] initial alpha temperature initialized to right values (SAC & PPO) --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 4 ++-- arenai_agent/src/agents/sac/sac_trainer.cpp | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index eb53a75a..0279f97c 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -44,8 +44,8 @@ namespace arenai::agent { critic(std::make_shared( vision_height, vision_width, nb_sensors, hidden_size_sensors, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous(std::make_shared(0.01f)), - alpha_discrete(std::make_shared(0.01f)), + alpha_continuous(std::make_shared(0.0001f)), + alpha_discrete(std::make_shared(0.001f)), continuous_target_entropy( std::make_shared(nb_continuous_actions, 0.1f)), discrete_target_entropy(std::make_shared(0.2f)), diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 60f6921a..93861554 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -49,8 +49,8 @@ namespace arenai::agent { vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, hidden_size_sensors, hidden_size_actions, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous(std::make_shared(0.01f)), - alpha_discrete(std::make_shared(0.01f)), + alpha_continuous(std::make_shared(0.0001f)), + alpha_discrete(std::make_shared(0.001f)), continuous_target_entropy( std::make_shared(nb_continuous_actions, 0.1f)), discrete_target_entropy(std::make_shared(0.2f)), From e65fca2091c8a21aa354e0d72a5c45160692bf61 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 12 Aug 2026 17:54:05 +0200 Subject: [PATCH 22/70] fix ppo : * train critic independently of kl stop for actor * update alpha once per train call (so higher learning rate) * higher target entropy sigma for continuous actions (SAC & PPO) --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 2 +- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 218 +++++++++++------- arenai_agent/src/agents/ppo/ppo_trainer.h | 28 +++ arenai_agent/src/agents/sac/sac_trainer.cpp | 4 +- arenai_agent/src/networks_utils/init.h | 2 +- 5 files changed, 165 insertions(+), 89 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 689d82dc..d3dc95c6 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -33,7 +33,7 @@ namespace arenai::agent { float target_kl = 0.02f; float grad_norm_max = 0.5f; // lr of the SAC-style adaptive entropy alphas (fixed targets: sigma 0.1, fire proba 0.2) - float alpha_learning_rate = 3e-4f; + float alpha_learning_rate = 1e-2f; int epochs = 4; int rollout_size = 30 * 30; int minibatch_size = 1024; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 0279f97c..2ca55b62 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -44,10 +44,10 @@ namespace arenai::agent { critic(std::make_shared( vision_height, vision_width, nb_sensors, hidden_size_sensors, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous(std::make_shared(0.0001f)), + alpha_continuous(std::make_shared(0.001f)), alpha_discrete(std::make_shared(0.001f)), continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.1f)), + std::make_shared(nb_continuous_actions, 0.2f)), discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), @@ -106,110 +106,158 @@ namespace arenai::agent { const auto nb_valid_rows = valid_idx.size(0); if (nb_valid_rows == 0) return; + const auto select = [&](const torch::Tensor &tensor, const torch::Tensor &idx) { + return tensor.index_select(0, idx).to(device); + }; + + float continuous_entropy_sum = 0.f; + float discrete_entropy_sum = 0.f; + int nb_actor_minibatches = 0; + bool kl_stop = false; - for (int e = 0; e < epochs && !kl_stop; e++) { + for (int e = 0; e < epochs; e++) { const auto perm = valid_idx.index_select(0, torch::randperm(nb_valid_rows)); for (int64_t start = 0; start < nb_valid_rows; start += minibatch_size) { const auto idx = perm.slice(0, start, std::min(start + minibatch_size, nb_valid_rows)); - const auto select = [&](const torch::Tensor &tensor) { - return tensor.index_select(0, idx).to(device); - }; - - const auto mb_vision = select(flat_vision); - const auto mb_proprioception = select(flat_proprioception); - const auto mb_continuous_actions = select(flat_continuous_actions); - const auto mb_discrete_actions = select(flat_discrete_actions); - const auto mb_old_log_probs = select(flat_old_log_probs); - const auto mb_advantages = select(flat_advantages); - const auto mb_returns = select(flat_returns); - - // policy: clipped surrogate on the joint (continuous x discrete) ratio - const auto [mu, sigma, discrete_proba] = actor->act(mb_vision, mb_proprioception); - - const auto curr_continuous_log_probs = - truncated_normal_log_pdf(mb_continuous_actions, mu, sigma).sum(-1, true); - - const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); - const auto curr_discrete_log_probs = - (mb_discrete_actions * torch::log(clamped_proba)).sum(-1, true); - - const auto ratio = torch::exp( - curr_continuous_log_probs + curr_discrete_log_probs - mb_old_log_probs); - - // approx KL (Schulman): E[(ratio - 1) - log ratio]; stop the update before - // this minibatch if the policy drifted too far from the rollout policy - const auto approx_kl = ((ratio - 1.f) - torch::log(ratio)).mean().item(); - kl_metric->add(approx_kl); - if (target_kl > 0.f && approx_kl > 1.5f * target_kl) { - kl_stop = true; - break; + // the observation is the bulk of the transfer: uploaded once, used by both + const auto mb_vision = select(flat_vision, idx); + const auto mb_proprioception = select(flat_proprioception, idx); + + // actor: skipped for the rest of the update once a minibatch drifted past + // the KL threshold + if (!kl_stop) { + const auto [kl_exceeded, continuous_entropy, discrete_entropy] = train_actor( + mb_vision, mb_proprioception, select(flat_continuous_actions, idx), + select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), + select(flat_advantages, idx)); + + continuous_entropy_sum += continuous_entropy; + discrete_entropy_sum += discrete_entropy; + nb_actor_minibatches++; + + kl_stop = kl_exceeded; } - const auto clipped_ratio = - torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); + // critic: keeps training through the whole update, the KL early stop is a + // policy criterion only + train_critic(mb_vision, mb_proprioception, select(flat_returns, idx)); + } + } + + // dual ascent, once per rollout on the mean entropy of the whole update: outside the + // epoch loop, an early KL stop can no longer freeze the entropy regulation + if (nb_actor_minibatches > 0) { + const auto nb_minibatches = static_cast(nb_actor_minibatches); + + train_alpha( + alpha_continuous, alpha_continuous_optim, continuous_target_entropy, + continuous_entropy_sum / nb_minibatches, alpha_continuous_metric); + train_alpha( + alpha_discrete, alpha_discrete_optim, discrete_target_entropy, + discrete_entropy_sum / nb_minibatches, alpha_discrete_metric); + } + } - const auto surrogate = - torch::min(ratio * mb_advantages, clipped_ratio * mb_advantages); + ActorUpdateResult PpoTrainer::train_actor( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions, + const torch::Tensor &old_log_probs, const torch::Tensor &advantages) const { - const auto continuous_entropy = truncated_normal_entropy(mu, sigma).sum(-1, true); - const auto discrete_entropy = multinomial_entropy(discrete_proba); + // policy: clipped surrogate on the joint (continuous x discrete) ratio + const auto [mu, sigma, discrete_proba] = actor->act(vision, proprioception); - const auto actor_loss = -torch::mean( - surrogate + alpha_continuous->alpha().detach() * continuous_entropy - + alpha_discrete->alpha().detach() * discrete_entropy); + const auto curr_continuous_log_probs = + truncated_normal_log_pdf(continuous_actions, mu, sigma).sum(-1, true); - actor_optim->zero_grad(); - actor_loss.backward(); - torch::nn::utils::clip_grad_norm_(actor->parameters(), grad_norm_max); - actor_optim->step(); + const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); + const auto curr_discrete_log_probs = + (discrete_actions * torch::log(clamped_proba)).sum(-1, true); - // dual ascent: entropy above target -> alpha decays toward 0 (stops pushing - // sigma up); entropy below target -> alpha grows (prevents entropy collapse) - const auto alpha_continuous_loss = torch::mean( - alpha_continuous->log_alpha() - * (continuous_entropy.detach() - continuous_target_entropy->target_entropy())); + const auto ratio = + torch::exp(curr_continuous_log_probs + curr_discrete_log_probs - old_log_probs); - alpha_continuous_optim->zero_grad(); - alpha_continuous_loss.backward(); - alpha_continuous_optim->step(); + const auto continuous_entropy = truncated_normal_entropy(mu, sigma).sum(-1, true); + const auto discrete_entropy = multinomial_entropy(discrete_proba); - const auto alpha_discrete_loss = torch::mean( - alpha_discrete->log_alpha() - * (discrete_entropy.detach() - discrete_target_entropy->target_entropy())); + const auto mean_continuous_entropy = continuous_entropy.mean().item(); + const auto mean_discrete_entropy = discrete_entropy.mean().item(); - alpha_discrete_optim->zero_grad(); - alpha_discrete_loss.backward(); - alpha_discrete_optim->step(); + continuous_entropy_metric->add(mean_continuous_entropy); + discrete_entropy_metric->add(mean_discrete_entropy); + sigma_metric->add(sigma.mean().item()); - // critic - const auto values = critic->value(mb_vision, mb_proprioception); - const auto critic_loss = torch::mse_loss(values, mb_returns, at::Reduction::Mean); + // approx KL (Schulman): E[(ratio - 1) - log ratio]; skip this minibatch when the + // policy drifted too far from the rollout policy + const auto approx_kl = torch::mean(ratio - 1.f - torch::log(ratio)).item(); + kl_metric->add(approx_kl); + if (target_kl > 0.f && approx_kl > 1.5f * target_kl) + return { + .kl_exceeded = true, + .continuous_entropy = mean_continuous_entropy, + .discrete_entropy = mean_discrete_entropy}; - critic_optim->zero_grad(); - critic_loss.backward(); - torch::nn::utils::clip_grad_norm_(critic->parameters(), grad_norm_max); - critic_optim->step(); + const auto clipped_ratio = torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); - // metrics - actor_mean_loss_metric->add(actor_loss.cpu().item()); - actor_std_loss_metric->add(actor_loss.cpu().item()); + const auto surrogate = torch::min(ratio * advantages, clipped_ratio * advantages); - critic_mean_loss_metric->add(critic_loss.cpu().item()); - critic_std_loss_metric->add(critic_loss.cpu().item()); + const auto actor_loss = -torch::mean( + surrogate + alpha_continuous->alpha().detach() * continuous_entropy + + alpha_discrete->alpha().detach() * discrete_entropy); - continuous_entropy_metric->add(continuous_entropy.mean().item()); - discrete_entropy_metric->add(discrete_entropy.mean().item()); - alpha_continuous_metric->add(alpha_continuous->alpha().item()); - alpha_discrete_metric->add(alpha_discrete->alpha().item()); - sigma_metric->add(sigma.mean().item()); + actor_optim->zero_grad(); + actor_loss.backward(); + torch::nn::utils::clip_grad_norm_(actor->parameters(), grad_norm_max); + actor_optim->step(); - clip_fraction_metric->add( - ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); - } - } + const auto loss_value = actor_loss.cpu().item(); + actor_mean_loss_metric->add(loss_value); + actor_std_loss_metric->add(loss_value); + + clip_fraction_metric->add( + ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); + + return { + .kl_exceeded = false, + .continuous_entropy = mean_continuous_entropy, + .discrete_entropy = mean_discrete_entropy}; + } + + void PpoTrainer::train_critic( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &returns) const { + + const auto values = critic->value(vision, proprioception); + const auto critic_loss = torch::mse_loss(values, returns, at::Reduction::Mean); + + critic_optim->zero_grad(); + critic_loss.backward(); + torch::nn::utils::clip_grad_norm_(critic->parameters(), grad_norm_max); + critic_optim->step(); + + const auto loss_value = critic_loss.cpu().item(); + critic_mean_loss_metric->add(loss_value); + critic_std_loss_metric->add(loss_value); + } + + void PpoTrainer::train_alpha( + const std::shared_ptr &alpha, + const std::shared_ptr &optim, + const std::shared_ptr &target_entropy, const float mean_entropy, + const std::shared_ptr &alpha_metric) { + + // entropy above target -> alpha decays toward 0 (stops pushing sigma up); + // entropy below target -> alpha grows (prevents entropy collapse) + const auto alpha_loss = + torch::mean(alpha->log_alpha() * (mean_entropy - target_entropy->target_entropy())); + + optim->zero_grad(); + alpha_loss.backward(); + optim->step(); + + alpha_metric->add(alpha->alpha().item()); } GaeResult PpoTrainer::compute_gae(const PpoRollout &rollout, const torch::Device device) const { @@ -260,9 +308,9 @@ namespace arenai::agent { const auto returns = advantages + values; const auto nb_valid = valids.sum().clamp_min(1.f); - const auto advantage_mean = (advantages * valids).sum() / nb_valid; + const auto advantage_mean = torch::sum(advantages * valids) / nb_valid; const auto advantage_std = - (((advantages - advantage_mean).square() * valids).sum() / nb_valid).sqrt(); + torch::sqrt(torch::sum(torch::square(advantages - advantage_mean) * valids) / nb_valid); advantages = (advantages - advantage_mean) / (advantage_std + EPSILON); return {.advantages = advantages, .returns = returns}; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 8153e687..f2c1863a 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -18,6 +18,13 @@ namespace arenai::agent { torch::Tensor returns; }; + struct ActorUpdateResult { + // the minibatch drifted past the KL threshold: no update was applied + bool kl_exceeded; + float continuous_entropy; + float discrete_entropy; + }; + class PpoTrainer final : public AbstractTrainer { public: PpoTrainer( @@ -89,6 +96,27 @@ namespace arenai::agent { void train() const; + // one backward pass on the actor for a single minibatch; the update is skipped + // when the minibatch already drifted past the KL threshold, its entropies are + // reported either way so the caller can feed the dual ascent + ActorUpdateResult train_actor( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions, + const torch::Tensor &old_log_probs, const torch::Tensor &advantages) const; + + // one backward pass on the critic for a single minibatch + void train_critic( + const torch::Tensor &vision, const torch::Tensor &proprioception, + const torch::Tensor &returns) const; + + // one backward pass of the dual ascent on a single entropy coefficient, fed with + // the mean entropy of the whole update + static void train_alpha( + const std::shared_ptr &alpha, + const std::shared_ptr &optim, + const std::shared_ptr &target_entropy, float mean_entropy, + const std::shared_ptr &alpha_metric); + // GAE advantages (normalized over the valid pairs) and value targets, // computed with the pre-update critic GaeResult compute_gae(const PpoRollout &rollout, torch::Device device) const; diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 93861554..57b3afbb 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -49,10 +49,10 @@ namespace arenai::agent { vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, hidden_size_sensors, hidden_size_actions, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous(std::make_shared(0.0001f)), + alpha_continuous(std::make_shared(0.001f)), alpha_discrete(std::make_shared(0.001f)), continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.1f)), + std::make_shared(nb_continuous_actions, 0.2f)), discrete_target_entropy(std::make_shared(0.2f)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), diff --git a/arenai_agent/src/networks_utils/init.h b/arenai_agent/src/networks_utils/init.h index 3c48325e..e667d3af 100644 --- a/arenai_agent/src/networks_utils/init.h +++ b/arenai_agent/src/networks_utils/init.h @@ -12,7 +12,7 @@ namespace arenai::agent { void init_hidden_weights(torch::nn::Module &module); void init_mu_output_weights(torch::nn::Module &module); - void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = 0.1f); + void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = 0.2f); void init_discrete_output_weights(torch::nn::Module &module, float initial_fire_probability = 0.2f); From e5b8d5c2bf6abb886065ce4e46db2ad18e169593 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Thu, 13 Aug 2026 01:53:53 +0200 Subject: [PATCH 23/70] centralized constants --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 7 ++++--- arenai_agent/src/agents/sac/sac_trainer.cpp | 8 +++++--- arenai_agent/src/networks/constants.h | 3 +++ arenai_agent/src/networks_utils/init.h | 8 +++++--- 4 files changed, 17 insertions(+), 9 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 2ca55b62..ae4c387d 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -46,9 +46,10 @@ namespace arenai::agent { vision_channels, group_norm_nums)), alpha_continuous(std::make_shared(0.001f)), alpha_discrete(std::make_shared(0.001f)), - continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.2f)), - discrete_target_entropy(std::make_shared(0.2f)), + continuous_target_entropy(std::make_shared( + nb_continuous_actions, TARGET_SIGMA)), + discrete_target_entropy( + std::make_shared(TARGET_FIRE_PROBABILITY)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_optim(std::make_unique( diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 57b3afbb..b6195c0f 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -11,6 +11,7 @@ #include "../../metrics/last_metric.h" #include "../../metrics/mean_metric.h" #include "../../metrics/std_metric.h" +#include "../../networks/constants.h" #include "../../networks_utils/print_module.h" #include "../../networks_utils/target_update.h" #include "../../networks_utils/torch_loader.h" @@ -51,9 +52,10 @@ namespace arenai::agent { group_norm_nums)), alpha_continuous(std::make_shared(0.001f)), alpha_discrete(std::make_shared(0.001f)), - continuous_target_entropy( - std::make_shared(nb_continuous_actions, 0.2f)), - discrete_target_entropy(std::make_shared(0.2f)), + continuous_target_entropy(std::make_shared( + nb_continuous_actions, TARGET_SIGMA)), + discrete_target_entropy( + std::make_shared(TARGET_FIRE_PROBABILITY)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_1_optim(std::make_unique( diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index 91a2122e..f24b8646 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -8,6 +8,9 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; + constexpr float TARGET_SIGMA = 0.3f; + constexpr float TARGET_FIRE_PROBABILITY = 0.4f; + constexpr float SIGMA_MIN = 1e-3f; constexpr float SIGMA_MAX = 1.f; }// namespace arenai::agent diff --git a/arenai_agent/src/networks_utils/init.h b/arenai_agent/src/networks_utils/init.h index e667d3af..341512fe 100644 --- a/arenai_agent/src/networks_utils/init.h +++ b/arenai_agent/src/networks_utils/init.h @@ -7,14 +7,16 @@ #include +#include "../networks/constants.h" + namespace arenai::agent { void init_hidden_weights(torch::nn::Module &module); void init_mu_output_weights(torch::nn::Module &module); - void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = 0.2f); - void - init_discrete_output_weights(torch::nn::Module &module, float initial_fire_probability = 0.2f); + void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = TARGET_SIGMA); + void init_discrete_output_weights( + torch::nn::Module &module, float initial_fire_probability = TARGET_FIRE_PROBABILITY); void init_value_output_weights(torch::nn::Module &module); From cada61192dcf5f887ecb25e6639a606b201ed8a0 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 14:29:32 +0200 Subject: [PATCH 24/70] * try without potential reward shaping * fix distance to closest enemy at shell trajectory * fix shell disambiguation (on pointer, not on fire pos) * smaller shell reserve (from 30 to 10) --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 21 ++- arenai_agent/src/agents/ppo/ppo_trainer.h | 2 + arenai_agent/src/core/train_environment.cpp | 32 ++--- arenai_agent/src/core/train_environment.h | 8 +- arenai_agent/src/main.cpp | 2 - arenai_agent/src/networks/constants.h | 8 +- arenai_agent/src/train.cpp | 29 +--- arenai_agent/src/train.h | 1 - .../tests/src/tests_agents/tests_ppo.cpp | 2 +- arenai_model/include/arenai_model/item.h | 4 + arenai_model/include/arenai_model/tank.h | 1 - arenai_model/src/item.cpp | 5 + arenai_model/src/tank/jolt_enemy_tank.cpp | 130 ++++++++---------- arenai_model/src/tank/jolt_enemy_tank.h | 15 +- arenai_model/src/tank/jolt_player_tank.cpp | 3 +- arenai_model/src/tank/jolt_tank.cpp | 13 +- arenai_model/src/tank/jolt_tank.h | 4 +- arenai_model/src/tank/parts/canon.cpp | 2 +- arenai_model/src/tank/parts/canon.h | 4 +- arenai_model/src/tank/parts/shell.cpp | 8 +- arenai_model/src/tank/parts/shell.h | 6 +- .../tests/src/tests_reward/tests_reward.cpp | 47 +++++++ 22 files changed, 176 insertions(+), 171 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index ae4c387d..4b5eda79 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -58,6 +58,7 @@ namespace arenai::agent { alpha_continuous->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), alpha_discrete_optim(std::make_unique( alpha_discrete->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), + training_reward_metric(std::make_shared("tr", metric_window_size)), actor_mean_loss_metric(std::make_shared("π_μ", metric_window_size)), actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), @@ -86,6 +87,8 @@ namespace arenai::agent { const auto rollout = rollout_buffer->get_rollout(); + training_reward_metric->add(rollout.rewards.mean().item()); + set_train(false); const auto [advantages, returns] = compute_gae(rollout, device); @@ -318,18 +321,12 @@ namespace arenai::agent { } std::vector> PpoTrainer::get_metrics() { - return { - actor_mean_loss_metric, - actor_std_loss_metric, - critic_mean_loss_metric, - critic_std_loss_metric, - continuous_entropy_metric, - discrete_entropy_metric, - alpha_continuous_metric, - alpha_discrete_metric, - sigma_metric, - clip_fraction_metric, - kl_metric}; + return {training_reward_metric, actor_mean_loss_metric, + actor_std_loss_metric, critic_mean_loss_metric, + critic_std_loss_metric, continuous_entropy_metric, + discrete_entropy_metric, alpha_continuous_metric, + alpha_discrete_metric, sigma_metric, + clip_fraction_metric, kl_metric}; } void PpoTrainer::save(const std::filesystem::path &output_folder) { diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index f2c1863a..7674a6c4 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -62,6 +62,8 @@ namespace arenai::agent { std::shared_ptr alpha_continuous_optim; std::shared_ptr alpha_discrete_optim; + std::shared_ptr training_reward_metric; + std::shared_ptr actor_mean_loss_metric; std::shared_ptr actor_std_loss_metric; diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index f4648878..54dc82a0 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -31,6 +31,7 @@ namespace arenai::agent { nb_frames_added_when_hit(static_cast(5.f / wanted_frequency)), nb_tanks(nb_tanks), nb_steps(0), done(nb_tanks, false), already_done(nb_tanks, false), max_episode_steps(max_episode_steps), + reward_metric(std::make_shared("r", 4 * nb_tanks * max_episode_steps, 3)), episode_step_mean_nb_metric(std::make_shared("s_μ", 32, 1)), episode_step_std_nb_metric(std::make_shared("s_σ", 32)), fire_metric(std::make_shared("fire", 256, 2)), @@ -110,10 +111,11 @@ namespace arenai::agent { } } - // detect winner + // detect winner and log reward for (int i = 0; i < step_result.size(); i++) { if (done[i]) continue; + // detact winner if (const long nb_not_done = std::ranges::count(done, false); nb_not_done == 1) { const auto &[state, reward, is_done] = step_result[i]; if (only_one_tank_alive()) @@ -122,6 +124,9 @@ namespace arenai::agent { done[i] = true; } + + // log reward + reward_metric->add(std::get<1>(step_result[i])); } nb_steps++; @@ -129,16 +134,6 @@ namespace arenai::agent { return step_result; } - std::vector TrainTankEnvironment::get_phi_vector() { - return apply_on_factories>( - [](const std::vector> &tanks) { - std::vector phi_vector; - phi_vector.reserve(tanks.size()); - for (const auto &tank: tanks) phi_vector.emplace_back(tank->get_phi(tanks)); - return phi_vector; - }); - } - void TrainTankEnvironment::on_draw( const std::vector> &model_matrices) {} @@ -184,15 +179,12 @@ namespace arenai::agent { } std::vector> TrainTankEnvironment::get_metrics() const { - return { - episode_step_mean_nb_metric, episode_step_std_nb_metric, fire_metric, hit_metric, - kill_metric}; - } - - std::vector TrainTankEnvironment::get_valid_mask() const { - std::vector valid(nb_tanks); - for (int i = 0; i < nb_tanks; i++) valid[i] = !already_done[i]; - return valid; + return {reward_metric, + episode_step_mean_nb_metric, + episode_step_std_nb_metric, + fire_metric, + hit_metric, + kill_metric}; } }// namespace arenai::agent diff --git a/arenai_agent/src/core/train_environment.h b/arenai_agent/src/core/train_environment.h index bacbdc61..e52cda01 100644 --- a/arenai_agent/src/core/train_environment.h +++ b/arenai_agent/src/core/train_environment.h @@ -21,14 +21,8 @@ namespace arenai::agent { std::vector> step(float time_delta, const std::vector &actions) override; - std::vector get_phi_vector(); - std::vector> get_metrics() const; - // tanks whose transition of the last step is a live one (not already done before - // the step) — mask for reward metrics, mirrors the rollout buffer's valid rows - std::vector get_valid_mask() const; - bool is_episode_terminated(); static void reset_singleton(); @@ -56,6 +50,8 @@ namespace arenai::agent { int max_episode_steps; + std::shared_ptr reward_metric; + std::shared_ptr episode_step_mean_nb_metric; std::shared_ptr episode_step_std_nb_metric; diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 489c7277..88ae26c3 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -24,7 +24,6 @@ int main(const int argc, char **argv) { parser.add_argument("--output_folder").required(); parser.add_argument("--resources_folder").required(); parser.add_argument("--max_episode_steps").scan<'i', int>().default_value(30 * 60 * 3); - parser.add_argument("--potential_reward_gamma").scan<'g', float>().default_value(0.997f); parser.add_argument("--nb_episodes").scan<'i', int>().default_value(2000); parser.add_argument("--save_every").scan<'i', int>().default_value(30 * 60 * 3 * 20); parser.add_argument("--cuda").default_value(false).implicit_value(true); @@ -77,7 +76,6 @@ int main(const int argc, char **argv) { .num_threads = parser.get("--vision_num_threads")}, {.output_folder = std::filesystem::path(parser.get("--output_folder")), .resources_folder = std::filesystem::path(parser.get("--resources_folder")), - .potential_reward_gamma = parser.get("--potential_reward_gamma"), .max_episode_steps = parser.get("--max_episode_steps"), .nb_episodes = parser.get("--nb_episodes"), .save_every = parser.get("--save_every"), diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index f24b8646..b70322f5 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -8,11 +8,11 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; - constexpr float TARGET_SIGMA = 0.3f; - constexpr float TARGET_FIRE_PROBABILITY = 0.4f; + constexpr float TARGET_SIGMA = 0.1f; + constexpr float TARGET_FIRE_PROBABILITY = 0.05f; - constexpr float SIGMA_MIN = 1e-3f; - constexpr float SIGMA_MAX = 1.f; + constexpr float SIGMA_MIN = 1e-4f; + constexpr float SIGMA_MAX = 2.f; }// namespace arenai::agent #endif//ARENAI_CONSTANTS_H diff --git a/arenai_agent/src/train.cpp b/arenai_agent/src/train.cpp index 05ed189b..4e4bafa8 100644 --- a/arenai_agent/src/train.cpp +++ b/arenai_agent/src/train.cpp @@ -66,12 +66,11 @@ namespace arenai::agent { AgentSaver saver(trainer, train_options.output_folder, train_options.save_every); // metrics - auto reward_mean_metric = std::make_shared("r", 256, 2, true); const auto sac_metrics = trainer->get_metrics(); const auto env_metrics = env->get_metrics(); - std::vector> metrics = {reward_mean_metric}; + std::vector> metrics; metrics.insert(metrics.end(), env_metrics.begin(), env_metrics.end()); metrics.insert(metrics.end(), sac_metrics.begin(), sac_metrics.end()); @@ -110,8 +109,6 @@ namespace arenai::agent { env->reset(spawn_width, spawn_height), environment_options.vision_height, environment_options.vision_width); - auto last_phi_tensor = torch::tensor(env->get_phi_vector()).unsqueeze(1); - while (!is_done) { std::vector actions_for_env; @@ -127,25 +124,16 @@ namespace arenai::agent { // step environment const auto steps = env->step(environment_options.wanted_frequency, actions_for_env); - const auto phi_tensor = torch::tensor(env->get_phi_vector()).unsqueeze(1); const auto [torch_next_states, torch_rewards, torch_are_done] = steps_to_tensor( steps, environment_options.vision_height, environment_options.vision_width); - const auto terminal_mask = torch::logical_not(torch_are_done); - const auto potential_reward = - terminal_mask * train_options.potential_reward_gamma * phi_tensor - - last_phi_tensor; - - const auto torch_final_reward = torch_rewards + potential_reward; - // complete the pending transition - maybe train - collector->on_transition(torch_final_reward, torch_are_done); + collector->on_transition(torch_rewards, torch_are_done); trainer->step(); // step ending stuff is_done = env->is_episode_terminated(); - last_phi_tensor = phi_tensor; vision = torch_next_states.vision; proprioception = torch_next_states.proprioception; @@ -156,19 +144,6 @@ namespace arenai::agent { saver.attempt_save(); metric_csv_saver.attempt_append_to_csv(); - // metrics: mean reward over the tanks still in play, dead tanks emit a - // post-mortem -1 every frame that would drown the signal - const auto valid_mask = env->get_valid_mask(); - float valid_reward_sum = 0.f; - int nb_valid = 0; - for (size_t i = 0; i < steps.size(); i++) { - if (!valid_mask[i]) continue; - valid_reward_sum += std::get<1>(steps[i]); - nb_valid++; - } - if (nb_valid > 0) - reward_mean_metric->add(valid_reward_sum / static_cast(nb_valid)); - // progress bar metrics display if (print_counter == print_tqdm_bar_every - 1) { std::stringstream stream; diff --git a/arenai_agent/src/train.h b/arenai_agent/src/train.h index 213fabc9..639f9f7d 100644 --- a/arenai_agent/src/train.h +++ b/arenai_agent/src/train.h @@ -15,7 +15,6 @@ namespace arenai::agent { struct TrainOptions { std::filesystem::path output_folder; std::filesystem::path resources_folder; - float potential_reward_gamma; int max_episode_steps; int nb_episodes; int save_every; diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index e24af445..7c439a98 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -65,7 +65,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 11); + ASSERT_EQ(metrics.size(), 12); } // ======================================================================== diff --git a/arenai_model/include/arenai_model/item.h b/arenai_model/include/arenai_model/item.h index ebe401ea..a43fc525 100644 --- a/arenai_model/include/arenai_model/item.h +++ b/arenai_model/include/arenai_model/item.h @@ -59,6 +59,10 @@ namespace arenai::model { bool is_dead() const; bool is_already_dead(); + // zeroes the health and flags the item as already dead: a wreck pays + // neither hit nor kill to whoever keeps shooting at it + void kill(); + float receive_damages(float damages); int consume_hits_received(); diff --git a/arenai_model/include/arenai_model/tank.h b/arenai_model/include/arenai_model/tank.h index f78c63cb..355da2b9 100644 --- a/arenai_model/include/arenai_model/tank.h +++ b/arenai_model/include/arenai_model/tank.h @@ -39,7 +39,6 @@ namespace arenai::model { class EnemyTank : virtual public Tank { public: virtual float get_reward(const std::vector> &tanks) = 0; - virtual float get_phi(const std::vector> &tanks) = 0; virtual std::vector get_proprioception() = 0; virtual bool has_hit_other_tank() = 0; virtual bool has_fired_shell() = 0; diff --git a/arenai_model/src/item.cpp b/arenai_model/src/item.cpp index 297f0bf3..71300601 100644 --- a/arenai_model/src/item.cpp +++ b/arenai_model/src/item.cpp @@ -42,6 +42,11 @@ namespace arenai::model { return already_dead; } + void LifeItem::kill() { + health_points = 0.f; + already_dead = true; + } + float LifeItem::receive_damages(const float damages) { const float new_health_point = std::max(health_points - damages, 0.f); const float received_damages = health_points - new_health_point; diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 1889dde9..65bee910 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -21,6 +21,24 @@ using namespace arenai; using namespace arenai::model; +namespace { + + // closest point of segment [a, b] to p, and its distance + float distance_to_segment( + const glm::vec3 &a, const glm::vec3 &b, const glm::vec3 &p, glm::vec3 &closest) { + const glm::vec3 ab = b - a; + const float length_squared = glm::length2(ab); + + const float t = + length_squared > 0.f ? std::clamp(glm::dot(p - a, ab) / length_squared, 0.f, 1.f) : 0.f; + + closest = a + t * ab; + + return glm::length(p - closest); + } + +}// namespace + namespace arenai::model { JoltEnemyTank::JoltEnemyTank( @@ -30,31 +48,21 @@ namespace arenai::model { const float wanted_frame_frequency) : JoltTank( engine, file_reader, tank_prefix_name, chassis_pos, wanted_frame_frequency, - [this](const ShellContactInfo &info, Item *item) { on_shell_contact(info, item); }, + [this](const ShellItem *shell, const ShellContactInfo &info, Item *item) { + on_shell_contact(shell, info, item); + }, [this](const std::shared_ptr &shell) { on_shell_fired(shell); }, [this] { return nb_shells > 0; }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), - curr_frame_upside_down(0), distance_scale(250.f), miss_distance_scale(10.f), - hit_reward_scale(0.5f), optimal_distance(75.f), aim_angle_scale(glm::radians(30.f)), - hit_received_cost(0.1f), initial_nb_shells(30), nb_shells(initial_nb_shells), + curr_frame_upside_down(0), miss_distance_scale(10.f), hit_reward_scale(0.5f), + // 10 shells at 6 shots/s: the reserve binds after ~1.5 s of spam instead of 5 s, + // so running dry still falls inside the discount horizon of the shots that + // caused it — a scarcity the policy can see (proprioception) rather than a + // per-shot penalty, which would make firing negative before aiming is learned + hit_received_cost(0.1f), initial_nb_shells(10), nb_shells(initial_nb_shells), shells_recharged_per_hit(5), is_dead_already_triggered(false), has_touch(false), has_fired(false) {} - float JoltEnemyTank::compute_aim_angle(const std::shared_ptr &other_tank) { - const auto canon_tr = get_canon()->get_model_matrix(); - const auto other_tr = other_tank->get_chassis()->get_model_matrix(); - - const auto canon_muzzle_pos = glm::vec3(canon_tr * glm::vec4(0.f, 0.f, 10.f, 1.f)); - const auto other_pos = glm::vec3(other_tr * glm::vec4(0.f, 0.f, 0.f, 1.f)); - - const glm::vec3 to_other = glm::normalize(other_pos - canon_muzzle_pos); - const auto forward = glm::normalize(glm::vec3(canon_tr * glm::vec4(0.f, 0.f, 1.f, 0.f))); - - const float d = std::clamp(glm::dot(forward, to_other), -1.f, 1.f); - - return std::acos(d); - } - float JoltEnemyTank::compute_hit_reward( const glm::vec3 &fire_pos, const glm::vec3 &enemy_pos, const glm::vec3 &shell_pos) const { @@ -72,20 +80,28 @@ namespace arenai::model { void JoltEnemyTank::update_closest_approach( TrackedShell &tracked, const glm::vec3 &shell_pos, const std::vector> &tanks) const { - const int nearest_index = get_nearest_enemy_index(tanks, shell_pos); - if (nearest_index == -1) return; - - const auto enemy_pos = glm::vec3( - tanks[nearest_index]->get_chassis()->get_model_matrix() - * glm::vec4(glm::vec3(0.f), 1.f)); - - if (const float distance = glm::length(shell_pos - enemy_pos); - distance < tracked.min_distance) { - tracked.min_distance = distance; - tracked.enemy_pos_at_t = enemy_pos; - tracked.shell_pos_at_t = shell_pos; - tracked.has_sample = true; + if (const int nearest_index = get_nearest_enemy_index(tanks, shell_pos); + nearest_index != -1) { + + const auto enemy_pos = glm::vec3( + tanks[nearest_index]->get_chassis()->get_model_matrix() + * glm::vec4(glm::vec3(0.f), 1.f)); + + // a shell covers ~8 m per frame, an order of magnitude more than the + // dispersion sigma: sampling positions alone aliases the miss distance, + // so measure against the segment actually travelled during the frame + glm::vec3 closest; + if (const float distance = + distance_to_segment(tracked.last_shell_pos, shell_pos, enemy_pos, closest); + distance < tracked.min_distance) { + tracked.min_distance = distance; + tracked.enemy_pos_at_t = enemy_pos; + tracked.shell_pos_at_t = closest; + tracked.has_sample = true; + } } + + tracked.last_shell_pos = shell_pos; } int JoltEnemyTank::get_nearest_enemy_index( @@ -111,46 +127,6 @@ namespace arenai::model { return best_i; } - float JoltEnemyTank::get_phi(const std::vector> &tanks) { - constexpr glm::vec4 world_center(glm::vec3(0.f), 1.f); - const glm::vec3 chassis_pos = get_chassis()->get_model_matrix() * world_center; - - std::vector scores; - std::vector logits; - - for (const auto &enemy: tanks) { - if (enemy.get() == this || enemy->is_dead()) continue; - - const glm::vec3 enemy_pos = enemy->get_chassis()->get_model_matrix() * world_center; - - const float distance = glm::length(enemy_pos - chassis_pos); - const float angle = compute_aim_angle(enemy); - - const float distance_score = - std::exp(-0.5f * std::pow((distance - optimal_distance) / distance_scale, 2.f)); - // sharp gaussian: pointing the canon at an enemy is the dense precursor of a - // hit; the previous (cos+1)/2 was still at 0.93 with 30 degrees of error - const float angle_score = std::exp(-0.5f * std::pow(angle / aim_angle_scale, 2.f)); - - scores.push_back(distance_score * angle_score); - logits.push_back(-distance / distance_scale); - } - - if (scores.empty()) return 0.f; - - const float max_logit = *std::ranges::max_element(logits); - float sum_exp = 0.f; - for (const float l: logits) sum_exp += std::exp(l - max_logit); - - float reward = 0.f; - for (std::size_t i = 0; i < scores.size(); ++i) { - const float weight = std::exp(logits[i] - max_logit) / sum_exp; - reward += weight * scores[i]; - } - - return reward; - } - float JoltEnemyTank::get_reward(const std::vector> &tanks) { // 1. flipped detection @@ -211,6 +187,8 @@ namespace arenai::model { tracked_shells.push_back( {.shell = shell, .fire_pos = shell->get_fire_position(), + // seeding the segment at the muzzle also covers the fire → first frame gap + .last_shell_pos = shell->get_fire_position(), .min_distance = std::numeric_limits::infinity(), .enemy_pos_at_t = glm::vec3(0.f), .shell_pos_at_t = glm::vec3(0.f), @@ -221,7 +199,8 @@ namespace arenai::model { .has_killed = false}); } - void JoltEnemyTank::on_shell_contact(const ShellContactInfo &shell_info, Item *item) { + void JoltEnemyTank::on_shell_contact( + const ShellItem *shell, const ShellContactInfo &shell_info, Item *item) { for (const auto &i: get_items()) if (i->get_name() == item->get_name()) return; @@ -243,7 +222,7 @@ namespace arenai::model { if (hit) nb_shells += shells_recharged_per_hit; for (auto &tracked: tracked_shells) { - if (tracked.has_final_pos || tracked.fire_pos != shell_info.fire_position) continue; + if (tracked.has_final_pos || tracked.shell.lock().get() != shell) continue; tracked.final_shell_pos = shell_info.current_position; tracked.has_final_pos = true; @@ -282,6 +261,9 @@ namespace arenai::model { if (is_dead() && !is_dead_already_triggered) { is_dead_already_triggered = true; remove_constraints_from_engine(); + // the wreck stays in the world as an obstacle, but its surviving parts must + // not pay hits, kills, shells nor survival frames to whoever keeps shooting it + kill_life_items(); } } diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index f64e97d0..3a63aa4d 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -17,7 +17,9 @@ namespace arenai::model { std::weak_ptr shell; glm::vec3 fire_pos; - // closest approach over the whole trajectory + // closest approach over the whole trajectory, measured against the segment + // travelled each frame (last_shell_pos → current position) + glm::vec3 last_shell_pos; float min_distance; glm::vec3 enemy_pos_at_t; glm::vec3 shell_pos_at_t; @@ -40,7 +42,6 @@ namespace arenai::model { float wanted_frame_frequency); float get_reward(const std::vector> &tanks) override; - float get_phi(const std::vector> &tanks) override; bool is_dead() override; bool is_first_frame_dead() override; @@ -65,14 +66,9 @@ namespace arenai::model { int max_frames_upside_down; int curr_frame_upside_down; - float distance_scale; - float miss_distance_scale; float hit_reward_scale; - float optimal_distance; - float aim_angle_scale; - float hit_received_cost; int initial_nb_shells; @@ -86,9 +82,8 @@ namespace arenai::model { std::vector tracked_shells; void on_shell_fired(const std::shared_ptr &shell); - void on_shell_contact(const ShellContactInfo &shell_info, Item *item); - - float compute_aim_angle(const std::shared_ptr &other_tank); + void + on_shell_contact(const ShellItem *shell, const ShellContactInfo &shell_info, Item *item); int get_nearest_enemy_index( const std::vector> &tanks, const glm::vec3 &pos) const; diff --git a/arenai_model/src/tank/jolt_player_tank.cpp b/arenai_model/src/tank/jolt_player_tank.cpp index eb8d3938..51aac22e 100644 --- a/arenai_model/src/tank/jolt_player_tank.cpp +++ b/arenai_model/src/tank/jolt_player_tank.cpp @@ -18,7 +18,8 @@ namespace arenai::model { const float wanted_frame_frequency) : JoltTank( engine, file_reader, tank_prefix_name, chassis_pos, wanted_frame_frequency, - [this](const ShellContactInfo &info, Item *item) { + // the player only counts hits, it does not track individual shells + [this](const ShellItem *, const ShellContactInfo &info, Item *item) { on_fired_shell_contact(info, item); }), killed_nb(0), hit_nb(0) {} diff --git a/arenai_model/src/tank/jolt_tank.cpp b/arenai_model/src/tank/jolt_tank.cpp index 9d5e08f4..cf32d197 100644 --- a/arenai_model/src/tank/jolt_tank.cpp +++ b/arenai_model/src/tank/jolt_tank.cpp @@ -28,7 +28,8 @@ namespace arenai::model { const std::shared_ptr &file_reader, const std::string &tank_prefix_name, glm::vec3 chassis_pos, const float wanted_frame_frequency, - const std::function &on_contact_callback, + const std::function + &on_contact_callback, const std::function &)> &on_shell_fired_callback, const std::function &can_fire_callback) : engine(engine), name(tank_prefix_name), camera(std::nullptr_t()), @@ -102,8 +103,10 @@ namespace arenai::model { auto canon_item = std::make_shared( tank_prefix_name, engine, file_reader, chassis_pos + turret_pos + canon_pos, canon_pos, scale * canon_scale, 100, turret->get_body(), wanted_frame_frequency, - [on_contact_callback](const glm::vec3 fire_pos, const glm::vec3 hit_pos, Item *item) { - on_contact_callback({fire_pos, hit_pos}, item); + [on_contact_callback]( + const ShellItem *shell, const glm::vec3 fire_pos, const glm::vec3 hit_pos, + Item *item) { + on_contact_callback(shell, {fire_pos, hit_pos}, item); }, on_shell_fired_callback, can_fire_callback); @@ -176,6 +179,10 @@ namespace arenai::model { std::shared_ptr JoltTank::get_canon() { return canon; } + void JoltTank::kill_life_items() const { + for (const auto life_item: life_items) life_item->kill(); + } + void JoltTank::remove_constraints_from_engine() const { for (const auto &item: jolt_items) engine.remove_jolt_item_constraints(item); } diff --git a/arenai_model/src/tank/jolt_tank.h b/arenai_model/src/tank/jolt_tank.h index 7d79501e..b5e54d79 100644 --- a/arenai_model/src/tank/jolt_tank.h +++ b/arenai_model/src/tank/jolt_tank.h @@ -24,7 +24,8 @@ namespace arenai::model { const std::shared_ptr &file_reader, const std::string &tank_prefix_name, glm::vec3 chassis_pos, float wanted_frame_frequency, - const std::function &on_contact_callback, + const std::function + &on_contact_callback, const std::function &)> &on_shell_fired_callback = [](const std::shared_ptr &) {}, const std::function &can_fire_callback = [] { return true; }); @@ -42,6 +43,7 @@ namespace arenai::model { protected: void remove_constraints_from_engine() const; + void kill_life_items() const; JoltPhysicEngine &engine; private: diff --git a/arenai_model/src/tank/parts/canon.cpp b/arenai_model/src/tank/parts/canon.cpp index 2624d4a2..0df4efcd 100644 --- a/arenai_model/src/tank/parts/canon.cpp +++ b/arenai_model/src/tank/parts/canon.cpp @@ -32,7 +32,7 @@ namespace arenai::model { const std::shared_ptr &file_reader, glm::vec3 pos, glm::vec3 rel_pos, glm::vec3 scale, float mass, JPH::Body *turret, const float wanted_frame_frequency, - const std::function &on_contact, + const std::function &on_contact, const std::function &)> &on_shell_fired, const std::function &can_fire) : LifeItem(5), ConvexItem( diff --git a/arenai_model/src/tank/parts/canon.h b/arenai_model/src/tank/parts/canon.h index 0fda787a..d6a8b2d5 100644 --- a/arenai_model/src/tank/parts/canon.h +++ b/arenai_model/src/tank/parts/canon.h @@ -31,7 +31,7 @@ namespace arenai::model { const std::shared_ptr &file_reader, glm::vec3 pos, glm::vec3 rel_pos, glm::vec3 scale, float mass, JPH::Body *turret, float wanted_frame_frequency, - const std::function &on_contact, + const std::function &on_contact, const std::function &)> &on_shell_fired, const std::function &can_fire); @@ -51,7 +51,7 @@ namespace arenai::model { JPH::Ref hinge; std::shared_ptr file_reader; bool will_fire; - std::function on_contact; + std::function on_contact; std::function &)> on_shell_fired; std::function can_fire; float wanted_frame_frequency; diff --git a/arenai_model/src/tank/parts/shell.cpp b/arenai_model/src/tank/parts/shell.cpp index 66f3658b..e2ece695 100644 --- a/arenai_model/src/tank/parts/shell.cpp +++ b/arenai_model/src/tank/parts/shell.cpp @@ -19,7 +19,8 @@ namespace arenai::model { const std::shared_ptr &file_reader, const glm::vec3 pos, const glm::quat rot, const glm::vec3 scale, const float mass, const float wanted_frame_frequency, - const std::function &contact_callback) + const std::function + &contact_callback) : LifeItem(1), ConvexItem(NAME, engine, load_shape(file_reader), pos, scale, mass, rot), contact_callback(contact_callback), nb_frames_alive(static_cast(20.f / wanted_frame_frequency)), start_pos(pos) {} @@ -29,7 +30,10 @@ namespace arenai::model { receive_damages(1); Item::on_contact(other); - contact_callback(get_fire_position(), get_current_position(), other); + // the shell identifies itself: the firing tank matches it against its tracked + // shells, two shells fired from the same muzzle pose being indistinguishable + // by position alone + contact_callback(this, get_fire_position(), get_current_position(), other); if (is_dead()) destroy(); } diff --git a/arenai_model/src/tank/parts/shell.h b/arenai_model/src/tank/parts/shell.h index 1e6bc782..50a977d8 100644 --- a/arenai_model/src/tank/parts/shell.h +++ b/arenai_model/src/tank/parts/shell.h @@ -25,8 +25,8 @@ namespace arenai::model { JoltPhysicEngine &engine, const std::shared_ptr &file_reader, glm::vec3 pos, glm::quat rot, glm::vec3 scale, float mass, float wanted_frame_frequency, - const std::function &contact_callback = - [](glm::vec3, glm::vec3, Item *) {}); + const std::function + &contact_callback = [](const ShellItem *, glm::vec3, glm::vec3, Item *) {}); void on_contact(Item *other) override; @@ -38,7 +38,7 @@ namespace arenai::model { glm::vec3 get_current_position(); private: - std::function contact_callback; + std::function contact_callback; int nb_frames_alive; glm::vec3 start_pos; diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index 0a9948f3..ccd3ea6b 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -169,6 +169,53 @@ TEST_F(RewardTest, RewardUnderOneAfterHit) { ASSERT_LE(reward_on_no_hit, 1.f) << "reward should be under 1.0 after no hitting an enemy"; } +// ======================================================================== +// get_reward — wrecks are not farmable +// ======================================================================== + +TEST_F(RewardTest, NoRewardWhenShootingAWreck) { + add_ground(); + // spawn tanks high enough so all parts start above ground and settle cleanly + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + + // settle on ground (300 frames = 5s at 60fps) + for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); + + const std::shared_ptr shared_a(tank_a.release()); + const std::shared_ptr shared_b(tank_b.release()); + + // kill tank_b by destroying a single part, then close its death as the environment + // does: the 8 surviving parts must stop paying anything + for (const auto items_b = shared_b->get_items(); const auto &item: items_b) { + if (auto *life = dynamic_cast(item.get())) { + life->receive_damages(1e6f); + break; + } + } + ASSERT_TRUE(shared_b->is_dead()); + shared_b->on_death(); + + const auto shells_ratio_before = shared_a->get_proprioception().back(); + + // fire from tank_a toward the wreck (canon points +Z by default) + constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); + + for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); + + const std::vector> tanks{shared_a, shared_b}; + + const float reward = shared_a->get_reward(tanks); + + ASSERT_FALSE(shared_a->has_hit_other_tank()) << "a wreck must not count as a hit"; + ASSERT_FLOAT_EQ(reward, 0.f) << "shooting a wreck must pay neither hit nor kill"; + + // the shell spent must not be given back: a wreck is not an ammo dump + ASSERT_LT(shared_a->get_proprioception().back(), shells_ratio_before) + << "a wreck must not recharge the shell reserve"; +} + // ======================================================================== // get_reward — NaN / Inf stability // ======================================================================== From 3d26b81e3b2bfafb858e75455100e198c4d886ec Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Thu, 13 Aug 2026 14:34:32 +0200 Subject: [PATCH 25/70] scientific format for reward metric --- arenai_agent/src/core/train_environment.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index 54dc82a0..6bb2a6f1 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -31,7 +31,8 @@ namespace arenai::agent { nb_frames_added_when_hit(static_cast(5.f / wanted_frequency)), nb_tanks(nb_tanks), nb_steps(0), done(nb_tanks, false), already_done(nb_tanks, false), max_episode_steps(max_episode_steps), - reward_metric(std::make_shared("r", 4 * nb_tanks * max_episode_steps, 3)), + reward_metric( + std::make_shared("r", 4 * nb_tanks * max_episode_steps, 3, true)), episode_step_mean_nb_metric(std::make_shared("s_μ", 32, 1)), episode_step_std_nb_metric(std::make_shared("s_σ", 32)), fire_metric(std::make_shared("fire", 256, 2)), From 18762742977d0d9b5933dd4775653a14b665d402 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 14:49:58 +0200 Subject: [PATCH 26/70] remove metric (wrong because add dead tank rewards) --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 21 +++++++++++-------- arenai_agent/src/agents/ppo/ppo_trainer.h | 2 -- .../tests/src/tests_agents/tests_ppo.cpp | 2 +- 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 4b5eda79..ae4c387d 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -58,7 +58,6 @@ namespace arenai::agent { alpha_continuous->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), alpha_discrete_optim(std::make_unique( alpha_discrete->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), - training_reward_metric(std::make_shared("tr", metric_window_size)), actor_mean_loss_metric(std::make_shared("π_μ", metric_window_size)), actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), @@ -87,8 +86,6 @@ namespace arenai::agent { const auto rollout = rollout_buffer->get_rollout(); - training_reward_metric->add(rollout.rewards.mean().item()); - set_train(false); const auto [advantages, returns] = compute_gae(rollout, device); @@ -321,12 +318,18 @@ namespace arenai::agent { } std::vector> PpoTrainer::get_metrics() { - return {training_reward_metric, actor_mean_loss_metric, - actor_std_loss_metric, critic_mean_loss_metric, - critic_std_loss_metric, continuous_entropy_metric, - discrete_entropy_metric, alpha_continuous_metric, - alpha_discrete_metric, sigma_metric, - clip_fraction_metric, kl_metric}; + return { + actor_mean_loss_metric, + actor_std_loss_metric, + critic_mean_loss_metric, + critic_std_loss_metric, + continuous_entropy_metric, + discrete_entropy_metric, + alpha_continuous_metric, + alpha_discrete_metric, + sigma_metric, + clip_fraction_metric, + kl_metric}; } void PpoTrainer::save(const std::filesystem::path &output_folder) { diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 7674a6c4..f2c1863a 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -62,8 +62,6 @@ namespace arenai::agent { std::shared_ptr alpha_continuous_optim; std::shared_ptr alpha_discrete_optim; - std::shared_ptr training_reward_metric; - std::shared_ptr actor_mean_loss_metric; std::shared_ptr actor_std_loss_metric; diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index 7c439a98..e24af445 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -65,7 +65,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 12); + ASSERT_EQ(metrics.size(), 11); } // ======================================================================== From 6591e170e9bfd135275597c0e8eb711a352ac2b7 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 15:04:42 +0200 Subject: [PATCH 27/70] refactor --- arenai_desktop/src/game.cpp | 265 +++++++++++++++++------------------- arenai_desktop/src/game.h | 13 +- arenai_desktop/src/main.cpp | 22 +-- 3 files changed, 152 insertions(+), 148 deletions(-) diff --git a/arenai_desktop/src/game.cpp b/arenai_desktop/src/game.cpp index 4db64a7c..0a63938f 100644 --- a/arenai_desktop/src/game.cpp +++ b/arenai_desktop/src/game.cpp @@ -22,166 +22,155 @@ using namespace arenai; namespace arenai::desktop { - namespace { - - enum class InGameOutcome { MainMenu, ExitGame, Retry }; - - // One game session: steps the environment until the window closes or - // the pause menu asks to leave. While paused, the simulation and the - // agent are simply not stepped; the frozen frame is re-rendered with - // the pause popup composited on top. - InGameOutcome run_game( - const GameOptions &game_options, const ModelOptions &model_options, - const gui::GameSettings &settings, - const std::shared_ptr &graphics_backend, - const std::unique_ptr &gui) { - const auto window = graphics_backend->get_window(); - - const std::shared_ptr sac_agent = - agent::ActorAgentFactory(model_options.hyper_parameters) - .get_agent( - model_options.vision_height, model_options.vision_width, - model::ENEMY_PROPRIOCEPTION_SIZE, model::ENEMY_NB_CONTINUOUS_ACTION, - model::ENEMY_NB_DISCRETE_ACTION); - - sac_agent->load(settings.sac_folder); - - const auto env = std::make_shared( - game_options.resources_folder, graphics_backend, settings.nb_tanks, - model_options.vision_height, model_options.vision_width, - game_options.wanted_frequency, settings.controller_kind); - - auto states = env->reset( - static_cast(settings.spawn_side), static_cast(settings.spawn_side)); - - // the router owns the window's input slots for the whole session; - // Escape / Start flip the pause state through toggle_requested - bool paused = false; - bool game_over = false; - bool toggle_requested = false; - - const auto router = std::make_shared( - env->keyboard_handler(), env->gamepad_handler(), gui->pause_input(), - gui->pause_gamepad_input(), [&toggle_requested] { toggle_requested = true; }); - window->set_keyboard_callback(router); - window->set_gamepad_callback(router); - - // in keyboard mode the game handler captures (and hides) the cursor - // itself; the gamepad handler never touches the cursor, so the - // application hides it for the whole game and the pause popup - // restores it - if (settings.controller_kind == ControllerKind::Gamepad) - window->set_cursor_mode(controller::CursorMode::Disabled); - - // the window has a single resize slot: while in game it feeds both - // the player renderer and the gui overlay - window->set_resize_callback([&gui, env](const int width, const int height) { - gui->on_window_resized(width, height); - env->resize(width, height); - }); + InGameOutcome run_game( + const GameOptions &game_options, const ModelOptions &model_options, + const gui::GameSettings &settings, + const std::shared_ptr &graphics_backend, + const std::unique_ptr &gui) { + const auto window = graphics_backend->get_window(); - const auto set_paused = [&](const bool value) { - paused = value; - router->set_paused(value); - if (value) { - gui->open_pause(); - window->set_cursor_mode(controller::CursorMode::Normal); - } else { - gui->close_pause(); - // in keyboard mode the game handler re-captures the cursor - // on its next event - if (settings.controller_kind == ControllerKind::Gamepad) - window->set_cursor_mode(controller::CursorMode::Disabled); - } - }; - - // the frame the player dies on freezes under the game-over popup, - // exactly like the pause: same input routing, same overlay loop — - // only the popup (and its actions) differ, and it cannot be - // toggled away - const auto set_game_over = [&] { - game_over = true; - router->set_paused(true); - gui->open_game_over(env->get_score()); + const std::shared_ptr sac_agent = + agent::ActorAgentFactory(model_options.hyper_parameters) + .get_agent( + model_options.vision_height, model_options.vision_width, + model::ENEMY_PROPRIOCEPTION_SIZE, model::ENEMY_NB_CONTINUOUS_ACTION, + model::ENEMY_NB_DISCRETE_ACTION); + + sac_agent->load(settings.sac_folder); + + const auto env = std::make_shared( + game_options.resources_folder, graphics_backend, settings.nb_tanks, + model_options.vision_height, model_options.vision_width, game_options.wanted_frequency, + settings.controller_kind); + + auto states = env->reset( + static_cast(settings.spawn_side), static_cast(settings.spawn_side)); + + // the router owns the window's input slots for the whole session; + // Escape / Start flip the pause state through toggle_requested + bool paused = false; + bool game_over = false; + bool toggle_requested = false; + + const auto router = std::make_shared( + env->keyboard_handler(), env->gamepad_handler(), gui->pause_input(), + gui->pause_gamepad_input(), [&toggle_requested] { toggle_requested = true; }); + window->set_keyboard_callback(router); + window->set_gamepad_callback(router); + + // in keyboard mode the game handler captures (and hides) the cursor + // itself; the gamepad handler never touches the cursor, so the + // application hides it for the whole game and the pause popup + // restores it + if (settings.controller_kind == ControllerKind::Gamepad) + window->set_cursor_mode(controller::CursorMode::Disabled); + + // the window has a single resize slot: while in game it feeds both + // the player renderer and the gui overlay + window->set_resize_callback([&gui, env](const int width, const int height) { + gui->on_window_resized(width, height); + env->resize(width, height); + }); + + const auto set_paused = [&](const bool value) { + paused = value; + router->set_paused(value); + if (value) { + gui->open_pause(); window->set_cursor_mode(controller::CursorMode::Normal); - }; - - auto outcome = InGameOutcome::ExitGame; - - // [ARENAI-DBG] temporary auto-repro - int dbg_auto_frames = -1; - if (const char *dbg = std::getenv("ARENAI_DEBUG_AUTOFRAMES")) - dbg_auto_frames = std::atoi(dbg); - int dbg_frame_count = 0; + } else { + gui->close_pause(); + // in keyboard mode the game handler re-captures the cursor + // on its next event + if (settings.controller_kind == ControllerKind::Gamepad) + window->set_cursor_mode(controller::CursorMode::Disabled); + } + }; - const auto frame_dt = - std::chrono::milliseconds(static_cast(game_options.wanted_frequency * 1000.f)); + // the frame the player dies on freezes under the game-over popup, + // exactly like the pause: same input routing, same overlay loop — + // only the popup (and its actions) differ, and it cannot be + // toggled away + const auto set_game_over = [&] { + game_over = true; + router->set_paused(true); + gui->open_game_over(env->get_score()); + window->set_cursor_mode(controller::CursorMode::Normal); + }; - while (!window->should_close()) { - if (dbg_auto_frames > 0 && dbg_frame_count++ >= dbg_auto_frames) break; - window->poll_events(); + auto outcome = InGameOutcome::ExitGame; - if (toggle_requested) { - toggle_requested = false; - if (!game_over) set_paused(!paused); - } + // [ARENAI-DBG] temporary auto-repro + int dbg_auto_frames = -1; + if (const char *dbg = std::getenv("ARENAI_DEBUG_AUTOFRAMES")) + dbg_auto_frames = std::atoi(dbg); + int dbg_frame_count = 0; - if (paused || game_over) { - // frozen scene + popup; pacing comes from the vsync - env->redraw(); - gui->render_pause_overlay(); - graphics_backend->present(); + const auto frame_dt = + std::chrono::milliseconds(static_cast(game_options.wanted_frequency * 1000.f)); - if (const auto action = gui->poll_pause_action(); - action == gui::PauseAction::Continue) - set_paused(false); - else if (action == gui::PauseAction::Retry) { - outcome = InGameOutcome::Retry; - break; - } else if (action == gui::PauseAction::MainMenu) { - outcome = InGameOutcome::MainMenu; - break; - } else if (action == gui::PauseAction::ExitGame) break; + while (!window->should_close()) { + if (dbg_auto_frames > 0 && dbg_frame_count++ >= dbg_auto_frames) break; + window->poll_events(); - continue; - } + if (toggle_requested) { + toggle_requested = false; + if (!game_over) set_paused(!paused); + } - auto last_time = std::chrono::steady_clock::now(); + if (paused || game_over) { + // frozen scene + popup; pacing comes from the vsync + env->redraw(); + gui->render_pause_overlay(); + graphics_backend->present(); - const auto action = - sac_agent->act(states, model_options.vision_height, model_options.vision_width); + if (const auto action = gui->poll_pause_action(); + action == gui::PauseAction::Continue) + set_paused(false); + else if (action == gui::PauseAction::Retry) { + outcome = InGameOutcome::Retry; + break; + } else if (action == gui::PauseAction::MainMenu) { + outcome = InGameOutcome::MainMenu; + break; + } else if (action == gui::PauseAction::ExitGame) break; + + continue; + } - const auto steps = env->step(game_options.wanted_frequency, action); + auto last_time = std::chrono::steady_clock::now(); - graphics_backend->present(); + const auto action = + sac_agent->act(states, model_options.vision_height, model_options.vision_width); - if (env->is_player_dead()) set_game_over(); + const auto steps = env->step(game_options.wanted_frequency, action); - states.clear(); + graphics_backend->present(); - for (const auto &[state, reward, done]: steps) states.push_back(state); + if (env->is_player_dead()) set_game_over(); - auto now = std::chrono::steady_clock::now(); - auto dt = now - last_time; + states.clear(); - std::this_thread::sleep_for( - std::max(frame_dt - dt, std::chrono::steady_clock::duration::zero())); - } + for (const auto &[state, reward, done]: steps) states.push_back(state); - gui->close_pause(); - gui->close_game_over(); - window->set_keyboard_callback(nullptr); - window->set_gamepad_callback(nullptr); - window->set_resize_callback([&gui](const int width, const int height) { - gui->on_window_resized(width, height); - }); + auto now = std::chrono::steady_clock::now(); + auto dt = now - last_time; - return outcome; + std::this_thread::sleep_for( + std::max(frame_dt - dt, std::chrono::steady_clock::duration::zero())); } - }// namespace + gui->close_pause(); + gui->close_game_over(); + window->set_keyboard_callback(nullptr); + window->set_gamepad_callback(nullptr); + window->set_resize_callback( + [&gui](const int width, const int height) { gui->on_window_resized(width, height); }); + + return outcome; + } - void game_loop(const GameOptions &game_options, const ModelOptions &model_options) { + void run_gui(const GameOptions &game_options, const ModelOptions &model_options) { // The view owns the window + GL context; the app only speaks the abstract // window/backend interface. const std::shared_ptr graphics_backend = diff --git a/arenai_desktop/src/game.h b/arenai_desktop/src/game.h index a6795024..992ca8af 100644 --- a/arenai_desktop/src/game.h +++ b/arenai_desktop/src/game.h @@ -4,10 +4,13 @@ #ifndef ARENAI_DESKTOP_GAME_H #define ARENAI_DESKTOP_GAME_H + #include #include #include +#include "./gui/menu.h" + namespace arenai::desktop { struct ModelOptions { @@ -26,7 +29,15 @@ namespace arenai::desktop { std::filesystem::path resources_folder; }; - void game_loop(const GameOptions &game_options, const ModelOptions &model_options); + enum class InGameOutcome { MainMenu, ExitGame, Retry }; + + InGameOutcome run_game( + const GameOptions &game_options, const ModelOptions &model_options, + const gui::GameSettings &settings, + const std::shared_ptr &graphics_backend, + const std::unique_ptr &gui); + + void run_gui(const GameOptions &game_options, const ModelOptions &model_options); }// namespace arenai::desktop diff --git a/arenai_desktop/src/main.cpp b/arenai_desktop/src/main.cpp index 365fd353..cb792101 100644 --- a/arenai_desktop/src/main.cpp +++ b/arenai_desktop/src/main.cpp @@ -19,7 +19,7 @@ using namespace arenai::desktop; // resources live next to the binary (copied there at build time, see // CMakeLists.txt), so they are resolved from the executable's folder — the // game can be launched from any working directory -std::filesystem::path executable_dir(const char *argv0) { +static std::filesystem::path executable_dir(const char *argv0) { #ifdef _WIN32 wchar_t buffer[MAX_PATH]; if (GetModuleFileNameW(nullptr, buffer, MAX_PATH) > 0) @@ -32,13 +32,13 @@ std::filesystem::path executable_dir(const char *argv0) { return std::filesystem::absolute(argv0).parent_path(); } -void trim_inplace(std::string &s) { +static void trim_inplace(std::string &s) { auto not_space = [](const unsigned char c) { return !std::isspace(c); }; s.erase(s.begin(), std::ranges::find_if(s, not_space)); s.erase(std::find_if(s.rbegin(), s.rend(), not_space).base(), s.end()); } -std::tuple parse_key_value(const std::string &value) { +static std::tuple parse_key_value(const std::string &value) { const std::regex regex_match(R"(^ *([^=]+)=\"?([^"]+)\"? *$)"); std::smatch match; @@ -80,12 +80,16 @@ int main(const int argc, char **argv) { const auto resources_folder = executable_dir(argv[0]) / "resources"; - game_loop( - {parser.get("--wanted_frequency"), parser.get("--window_width"), - parser.get("--window_height"), resources_folder}, - {parser.get("--vision_height"), parser.get("--vision_width"), hyper_params, - resources_folder / "trained_models" / "ppo-thin-256x128_save_6", - parser.get("--cuda")}); + run_gui( + {.wanted_frequency = parser.get("--wanted_frequency"), + .window_width = parser.get("--window_width"), + .window_height = parser.get("--window_height"), + .resources_folder = resources_folder}, + {.vision_height = parser.get("--vision_height"), + .vision_width = parser.get("--vision_width"), + .hyper_parameters = hyper_params, + .state_dict_folder = resources_folder / "trained_models" / "ppo-thin-256x128_save_6", + .cuda = parser.get("--cuda")}); return 0; } From a67dc3118d2f8fa72fc589646e28c92d84b529d3 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 15:34:00 +0200 Subject: [PATCH 28/70] add clang-tidy to ignore nodiscard --- .clang-tidy | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 .clang-tidy diff --git a/.clang-tidy b/.clang-tidy new file mode 100644 index 00000000..d4821959 --- /dev/null +++ b/.clang-tidy @@ -0,0 +1,7 @@ +--- +# Checks désactivés pour tout le projet (clang-tidy n'est utilisé que par l'IDE : +# ni le build CMake ni la CI ne le lancent). +# +# modernize-use-nodiscard : réclame [[nodiscard]] sur toute méthode const +# renvoyant une valeur — trop bruyant pour les nombreux getters du projet. +Checks: '-modernize-use-nodiscard' From 399f068cbcd2c91dc5e87b1b638177090d66f857 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 16:12:06 +0200 Subject: [PATCH 29/70] fix warnings --- arenai_model/src/items/height_map.cpp | 4 +- arenai_model/src/tank/jolt_tank.cpp | 18 +++++---- arenai_model/src/tank/jolt_tank.h | 2 +- arenai_model/src/tank/parts/canon.cpp | 7 +++- arenai_model/src/tank/parts/shell.h | 2 +- arenai_model/src/tank/parts/turret.cpp | 7 +++- arenai_model/src/tank/parts/wheel.cpp | 7 +++- .../src/tests_enemy_tank/tests_enemy_tank.cpp | 31 ++++++++++++---- .../tests_player_tank/tests_player_tank.cpp | 15 ++++++-- .../tests_proprioception.cpp | 12 +++--- .../tests/src/tests_reward/tests_reward.cpp | 37 +++++++++++++------ .../tests/src/tests_shell/tests_shell.cpp | 32 ++++++++++++---- 12 files changed, 121 insertions(+), 53 deletions(-) diff --git a/arenai_model/src/items/height_map.cpp b/arenai_model/src/items/height_map.cpp index a59e77e3..b3cb7f10 100644 --- a/arenai_model/src/items/height_map.cpp +++ b/arenai_model/src/items/height_map.cpp @@ -68,8 +68,8 @@ namespace arenai::model { auto push_triangle = [&](const glm::vec3 &p0, const glm::vec3 &p1, const glm::vec3 &p2) { - const glm::vec3 normal = glm::cross(p1 - p0, p2 - p0); - if (normal.y >= 0.f) triangles.emplace_back(to_jolt(p0), to_jolt(p1), to_jolt(p2)); + if (const glm::vec3 normal = glm::cross(p1 - p0, p2 - p0); normal.y >= 0.f) + triangles.emplace_back(to_jolt(p0), to_jolt(p1), to_jolt(p2)); else triangles.emplace_back(to_jolt(p0), to_jolt(p2), to_jolt(p1)); }; diff --git a/arenai_model/src/tank/jolt_tank.cpp b/arenai_model/src/tank/jolt_tank.cpp index cf32d197..268c121c 100644 --- a/arenai_model/src/tank/jolt_tank.cpp +++ b/arenai_model/src/tank/jolt_tank.cpp @@ -61,9 +61,9 @@ namespace arenai::model { for (auto &[wheel_name, wheel_pos, angle_factor]: front_wheel_config) { auto wheel = std::make_shared( - tank_prefix_name + "_" + wheel_name, engine, file_reader, wheel_pos + chassis_pos, - wheel_pos, wheel_scale, wheel_mass, chassis_item->get_body(), front_axle_z, - angle_factor); + std::format("{}_{}", tank_prefix_name, wheel_name), engine, file_reader, + wheel_pos + chassis_pos, wheel_pos, wheel_scale, wheel_mass, + chassis_item->get_body(), front_axle_z, angle_factor); jolt_items.push_back(wheel); items.push_back(wheel); @@ -77,8 +77,9 @@ namespace arenai::model { for (auto &[wheel_name, wheel_pos]: wheel_config) { auto wheel = std::make_shared( - tank_prefix_name + "_" + wheel_name, engine, file_reader, wheel_pos + chassis_pos, - wheel_pos, wheel_scale, wheel_mass, chassis_item->get_body(), front_axle_z); + std::format("{}_{}", tank_prefix_name, wheel_name), engine, file_reader, + wheel_pos + chassis_pos, wheel_pos, wheel_scale, wheel_mass, + chassis_item->get_body(), front_axle_z); jolt_items.push_back(wheel); items.push_back(wheel); @@ -106,7 +107,8 @@ namespace arenai::model { [on_contact_callback]( const ShellItem *shell, const glm::vec3 fire_pos, const glm::vec3 hit_pos, Item *item) { - on_contact_callback(shell, {fire_pos, hit_pos}, item); + on_contact_callback( + shell, {.fire_position = fire_pos, .current_position = hit_pos}, item); }, on_shell_fired_callback, can_fire_callback); @@ -152,7 +154,7 @@ namespace arenai::model { // register with engine for (const auto &item: jolt_items) engine.add_jolt_item(item); - engine.add_jolt_item_producer([c = canon_item]() { return c->produce_jolt_items(); }); + engine.add_jolt_item_producer([c = canon_item] { return c->produce_jolt_items(); }); } std::shared_ptr JoltTank::get_camera() { return camera; } @@ -169,7 +171,7 @@ namespace arenai::model { return std::ranges::any_of(life_items, [](const LifeItem *li) { return li->is_dead(); }); } - int JoltTank::get_received_hits() { + int JoltTank::get_received_hits() const { int hits = 0; for (const auto life_item: life_items) hits += life_item->consume_hits_received(); return hits; diff --git a/arenai_model/src/tank/jolt_tank.h b/arenai_model/src/tank/jolt_tank.h index b5e54d79..7a9a010c 100644 --- a/arenai_model/src/tank/jolt_tank.h +++ b/arenai_model/src/tank/jolt_tank.h @@ -35,7 +35,7 @@ namespace arenai::model { std::vector> get_controllers() override; std::map> load_shell_shapes() const override; bool is_dead() override; - int get_received_hits(); + int get_received_hits() const; std::shared_ptr get_chassis() override; std::shared_ptr get_canon() override; diff --git a/arenai_model/src/tank/parts/canon.cpp b/arenai_model/src/tank/parts/canon.cpp index 0df4efcd..9c14fc8e 100644 --- a/arenai_model/src/tank/parts/canon.cpp +++ b/arenai_model/src/tank/parts/canon.cpp @@ -55,8 +55,11 @@ namespace arenai::model { settings.mHingeAxis2 = JPH::Vec3::sAxisX(); settings.mNormalAxis2 = JPH::Vec3::sAxisY(); - hinge = - static_cast(settings.Create(*turret, *ConvexItem::get_body())); + auto *constraint = settings.Create(*turret, *ConvexItem::get_body()); + // Jolt is built without RTTI (-fno-rtti): dynamic_cast would not link. The dynamic + // type is guaranteed by the settings object the constraint was created from. + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-static-cast-downcast) + hinge = static_cast(constraint); hinge->SetMotorState(JPH::EMotorState::Position); hinge->SetTargetAngle(angle); diff --git a/arenai_model/src/tank/parts/shell.h b/arenai_model/src/tank/parts/shell.h index 50a977d8..c5df60c5 100644 --- a/arenai_model/src/tank/parts/shell.h +++ b/arenai_model/src/tank/parts/shell.h @@ -30,7 +30,7 @@ namespace arenai::model { void on_contact(Item *other) override; - inline const static std::string NAME = "shell_item"; + static constexpr std::string NAME = "shell_item"; void tick() override; diff --git a/arenai_model/src/tank/parts/turret.cpp b/arenai_model/src/tank/parts/turret.cpp index 0e2170db..bd29f561 100644 --- a/arenai_model/src/tank/parts/turret.cpp +++ b/arenai_model/src/tank/parts/turret.cpp @@ -35,8 +35,11 @@ namespace arenai::model { settings.mHingeAxis2 = JPH::Vec3::sAxisY(); settings.mNormalAxis2 = JPH::Vec3::sAxisX(); - hinge = - static_cast(settings.Create(*chassis, *ConvexItem::get_body())); + auto *constraint = settings.Create(*chassis, *ConvexItem::get_body()); + // Jolt is built without RTTI (-fno-rtti): dynamic_cast would not link. The dynamic + // type is guaranteed by the settings object the constraint was created from. + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-static-cast-downcast) + hinge = static_cast(constraint); // like Bullet's limit-less hinge: free until the first input engages // the servo } diff --git a/arenai_model/src/tank/parts/wheel.cpp b/arenai_model/src/tank/parts/wheel.cpp index 00339b71..9051835f 100644 --- a/arenai_model/src/tank/parts/wheel.cpp +++ b/arenai_model/src/tank/parts/wheel.cpp @@ -62,8 +62,11 @@ namespace arenai::model { settings.mMotorSettings[EAxis::RotationX].mMinTorqueLimit = -2e4f; settings.mMotorSettings[EAxis::RotationX].mMaxTorqueLimit = 2e4f; - hinge = static_cast( - settings.Create(*chassis, *ConvexItem::get_body())); + auto *constraint = settings.Create(*chassis, *ConvexItem::get_body()); + // Jolt is built without RTTI (-fno-rtti): dynamic_cast would not link. The dynamic + // type is guaranteed by the settings object the constraint was created from. + // NOLINTNEXTLINE(cppcoreguidelines-pro-type-static-cast-downcast) + hinge = static_cast(constraint); hinge->SetMotorState(EAxis::TranslationY, JPH::EMotorState::Position); hinge->SetTargetPositionCS(JPH::Vec3(0.f, -0.2f, 0.f)); diff --git a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp index 62fe0c00..fe84d4f3 100644 --- a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp +++ b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp @@ -104,12 +104,15 @@ TEST_F(EnemyTankTest, RewardWhenAllEnemiesDeadAndShellFired) { ASSERT_TRUE(shared_b->is_dead()); // fire from tank_a — shell will hit the dead tank or ground - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); - const std::vector> tanks{shared_a, shared_b}; + const std::vector tanks{shared_a, shared_b}; // get_nearest_enemy_index should return -1 (all dead) // reward should not crash and should be 0 (no valid target) @@ -127,12 +130,15 @@ TEST_F(EnemyTankTest, RewardNoNaNWhenAloneInTankList) { const std::shared_ptr shared_tank(tank.release()); // fire a shell that will hit the ground - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_tank->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); - const std::vector> tanks{shared_tank}; + const std::vector tanks{shared_tank}; const float reward = shared_tank->get_reward(tanks); ASSERT_FALSE(std::isnan(reward)) << "reward should not be NaN when alone"; @@ -153,12 +159,18 @@ TEST_F(EnemyTankTest, ShellHitsGroundNoRewardNoCrash) { const std::shared_ptr shared_tank(tank.release()); // tilt canon downward to ensure it hits the ground - constexpr user_input aim_down{{0.f, 0.f}, {0.f, 1.f}, {false}}; + constexpr user_input aim_down{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 1.f}, + .fire_button = {false}}; for (const auto &ctrl: shared_tank->get_controllers()) ctrl->apply_input(aim_down); for (const auto &ctrl: shared_tank->get_controllers()) ctrl->apply_input(aim_down); for (const auto &ctrl: shared_tank->get_controllers()) ctrl->apply_input(aim_down); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_tank->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); @@ -168,7 +180,7 @@ TEST_F(EnemyTankTest, ShellHitsGroundNoRewardNoCrash) { << "hitting the ground should not count as hitting another tank"; // but last_shoot_info should still be set — reward should not crash - std::vector> tanks{shared_tank}; + const std::vector tanks{shared_tank}; const float reward = shared_tank->get_reward(tanks); ASSERT_FALSE(std::isnan(reward)); } @@ -207,7 +219,10 @@ TEST_F(EnemyTankTest, HasHitOtherTankResetsAfterCall) { const std::shared_ptr shared_a(tank_a.release()); std::shared_ptr shared_b(tank_b.release()); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp b/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp index d76a8ade..a1b1162f 100644 --- a/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp +++ b/arenai_model/tests/src/tests_player_tank/tests_player_tank.cpp @@ -33,7 +33,10 @@ TEST_F(PlayerTankTest, ScoreIncreasesOnHit) { for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: player->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); @@ -53,7 +56,10 @@ TEST_F(PlayerTankTest, ScoreHigherOnKillThanHit) { if (auto *life = dynamic_cast(item.get())) { life->receive_damages(9.f); } } - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: player->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); @@ -70,7 +76,10 @@ TEST_F(PlayerTankTest, ScoreDoesNotIncreaseOnSelfHit) { for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); // fire without any enemy — shell should hit the ground or expire - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: player->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 1300; i++) engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp b/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp index d1d9edbe..3d6ae9d4 100644 --- a/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp +++ b/arenai_model/tests/src/tests_proprioception/tests_proprioception.cpp @@ -22,7 +22,7 @@ TEST_F(ProprioceptionTest, ProprioceptionSizeCorrect) { engine->step(1.f / 60.f); - std::shared_ptr shared_tank(tank.release()); + const std::shared_ptr shared_tank(tank.release()); const auto proprio = shared_tank->get_proprioception(); ASSERT_EQ(proprio.size(), ENEMY_PROPRIOCEPTION_SIZE); @@ -34,7 +34,7 @@ TEST_F(ProprioceptionTest, ProprioceptionNoNaN) { engine->step(1.f / 60.f); - std::shared_ptr shared_tank(tank.release()); + const std::shared_ptr shared_tank(tank.release()); const auto proprio = shared_tank->get_proprioception(); for (int i = 0; i < proprio.size(); i++) { @@ -49,7 +49,7 @@ TEST_F(ProprioceptionTest, ProprioceptionNoInfinity) { for (int i = 0; i < 10; i++) engine->step(1.f / 60.f); - std::shared_ptr shared_tank(tank.release()); + const std::shared_ptr shared_tank(tank.release()); const auto proprio = shared_tank->get_proprioception(); for (int i = 0; i < proprio.size(); i++) { @@ -63,7 +63,7 @@ TEST_F(ProprioceptionTest, ProprioceptionContainsSubItemRelativePositions) { engine->step(1.f / 60.f); - std::shared_ptr shared_tank(tank.release()); + const std::shared_ptr shared_tank(tank.release()); const auto proprio = shared_tank->get_proprioception(); // after the chassis data (12 floats), each sub-item contributes 15 floats: @@ -93,7 +93,7 @@ TEST_F(ProprioceptionTest, ProprioceptionConsistentSize) { engine->step(1.f / 60.f); - std::shared_ptr shared_tank(tank.release()); + const std::shared_ptr shared_tank(tank.release()); const auto proprio_1 = shared_tank->get_proprioception(); @@ -110,7 +110,7 @@ TEST_F(ProprioceptionTest, ProprioceptionForwardAndUpVectorsValid) { engine->step(1.f / 60.f); - std::shared_ptr shared_tank(tank.release()); + const std::shared_ptr shared_tank(tank.release()); const auto proprio = shared_tank->get_proprioception(); // elements [3..5] are chassis forward direction, [6..8] are chassis up direction diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index ccd3ea6b..b1127175 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -26,7 +26,7 @@ TEST_F(RewardTest, RewardZeroWhenAliveNoShot) { engine->step(1.f / 60.f); - const std::vector> tanks{ + const std::vector tanks{ std::shared_ptr(tank_a.release()), std::shared_ptr(tank_b.release())}; const float reward_a = tanks[0]->get_reward(tanks); @@ -45,7 +45,7 @@ TEST_F(RewardTest, RewardNegativeWhenDead) { engine->step(1.f / 60.f); - std::vector> tanks{ + const std::vector tanks{ std::shared_ptr(tank_a.release()), std::shared_ptr(tank_b.release())}; // damage chassis enough to kill it @@ -70,7 +70,7 @@ TEST_F(RewardTest, DeathPenaltyIsMinusOne) { engine->step(1.f / 60.f); - const std::vector> tanks{ + const std::vector tanks{ std::shared_ptr(tank_a.release()), std::shared_ptr(tank_b.release())}; // kill by damage → normal death penalty @@ -105,12 +105,15 @@ TEST_F(RewardTest, RewardPositiveOnHit) { const std::shared_ptr shared_b(tank_b.release()); // fire from tank_a toward tank_b (canon points +Z by default) - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); - const std::vector> tanks{shared_a, shared_b}; + const std::vector tanks{shared_a, shared_b}; const float reward = shared_a->get_reward(tanks); @@ -136,12 +139,15 @@ TEST_F(RewardTest, RewardUnderOneAfterHit) { const std::shared_ptr shared_b(tank_b.release()); // fire from tank_a toward tank_b (canon points +Z by default) - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); - const std::vector> tanks{shared_a, shared_b}; + const std::vector tanks{shared_a, shared_b}; const float reward_on_hit = shared_a->get_reward(tanks); @@ -154,7 +160,10 @@ TEST_F(RewardTest, RewardUnderOneAfterHit) { << "reward should be greater than or equal to 1.0 after hitting an enemy"; // no fire, reward under 1.0 - constexpr user_input no_fire_input{{0.f, 0.f}, {0.f, 0.f}, {false}}; + constexpr user_input no_fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {false}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(no_fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); @@ -199,12 +208,15 @@ TEST_F(RewardTest, NoRewardWhenShootingAWreck) { const auto shells_ratio_before = shared_a->get_proprioception().back(); // fire from tank_a toward the wreck (canon points +Z by default) - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); - const std::vector> tanks{shared_a, shared_b}; + const std::vector tanks{shared_a, shared_b}; const float reward = shared_a->get_reward(tanks); @@ -228,7 +240,10 @@ TEST_F(RewardTest, ZeroRewardWithEmptyTankList) { const std::shared_ptr shared_tank(tank.release()); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_tank->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); diff --git a/arenai_model/tests/src/tests_shell/tests_shell.cpp b/arenai_model/tests/src/tests_shell/tests_shell.cpp index e705363f..7eb2fa3d 100644 --- a/arenai_model/tests/src/tests_shell/tests_shell.cpp +++ b/arenai_model/tests/src/tests_shell/tests_shell.cpp @@ -25,7 +25,10 @@ TEST_F(ShellTest, FireCreatesShellItem) { const int count_before = static_cast(engine->get_items().size()); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: tank->get_controllers()) ctrl->apply_input(fire_input); engine->step(1.f / 60.f); @@ -43,7 +46,10 @@ TEST_F(ShellTest, ShellDestroyedAfterLifetime) { const int count_before_fire = static_cast(engine->get_items().size()); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: tank->get_controllers()) ctrl->apply_input(fire_input); engine->step(1.f / 60.f); @@ -70,7 +76,10 @@ TEST_F(ShellTest, ShellHitsEnemyTank) { const std::shared_ptr shared_a(tank_a.release()); std::shared_ptr shared_b(tank_b.release()); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); @@ -90,7 +99,10 @@ TEST_F(ShellTest, ShellDestroyedOnContact) { const std::shared_ptr shared_a(tank_a.release()); std::shared_ptr shared_b(tank_b.release()); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); engine->step(1.f / 60.f); @@ -113,7 +125,10 @@ TEST_F(ShellTest, NoFireNoNewItems) { const int count_before = static_cast(engine->get_items().size()); - constexpr user_input no_fire{{0.f, 1.f}, {0.f, 0.f}, {false}}; + constexpr user_input no_fire{ + .left_joystick = {.x = 0.f, .y = 1.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {false}}; for (const auto &ctrl: tank->get_controllers()) ctrl->apply_input(no_fire); engine->step(1.f / 60.f); @@ -133,12 +148,15 @@ TEST_F(ShellTest, ShellContactCallbackSetsReward) { const std::shared_ptr shared_a(tank_a.release()); const std::shared_ptr shared_b(tank_b.release()); - constexpr user_input fire_input{{0.f, 0.f}, {0.f, 0.f}, {true}}; + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; for (const auto &ctrl: shared_a->get_controllers()) ctrl->apply_input(fire_input); for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); - const std::vector> tanks{shared_a, shared_b}; + const std::vector tanks{shared_a, shared_b}; ASSERT_TRUE(shared_a->has_hit_other_tank()) << "shell must hit for reward test"; From 29b636b3b2aa984b1344dbb5b245461eb8e1ffb8 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 16:32:23 +0200 Subject: [PATCH 30/70] fix warnings --- arenai_model/include/arenai_model/constants.h | 6 ++---- arenai_model/src/jolt_engine.cpp | 10 ++++------ arenai_model/src/tank/jolt_tank.cpp | 8 ++++---- arenai_model/src/tank/parts/canon.cpp | 3 +-- arenai_model/src/tank/parts/turret.cpp | 1 - arenai_model/src/tank/parts/wheel.cpp | 2 -- .../tests_enemy_tank/tests_enemy_tank.h | 2 -- .../tests_player_tank/tests_player_tank.h | 2 -- .../tests_proprioception/tests_proprioception.h | 2 -- .../arenai_model_tests/tests_reward/tests_reward.h | 2 -- .../arenai_model_tests/tests_shell/tests_shell.h | 2 -- .../arenai_model_tests/utils/engine_test_fixture.h | 1 - .../tests/src/tests_enemy_tank/tests_enemy_tank.cpp | 1 - arenai_model/tests/src/tests_item/tests_life_item.cpp | 2 +- arenai_model/tests/src/tests_reward/tests_reward.cpp | 1 - 15 files changed, 12 insertions(+), 33 deletions(-) diff --git a/arenai_model/include/arenai_model/constants.h b/arenai_model/include/arenai_model/constants.h index 08a14c27..f1067153 100644 --- a/arenai_model/include/arenai_model/constants.h +++ b/arenai_model/include/arenai_model/constants.h @@ -5,18 +5,16 @@ #ifndef ARENAI_MODEL_CONSTANTS_H #define ARENAI_MODEL_CONSTANTS_H -#include - namespace arenai::model { - constexpr float WHEEL_RADIAL_VELOCITY = static_cast(M_PI) * 5.f; + constexpr float WHEEL_RADIAL_VELOCITY = std::numbers::pi * 5.f; // (pos, vel, forward, up, ang_vel) * (9 items: 6 wheels, 1 chassis, 1 turret, 1 canon) - chassis pos + remaining shells ratio constexpr int ENEMY_PROPRIOCEPTION_SIZE = (3 + 3 + 3 + 3 + 3) * (6 + 3) - 3 + 1; constexpr int ENEMY_NB_CONTINUOUS_ACTION = 2 + 2; constexpr int ENEMY_NB_DISCRETE_ACTION = 2; - constexpr float ENEMY_TURRET_RADIAL_VELOCITY = static_cast(M_PI) * 1.f; + constexpr float ENEMY_TURRET_RADIAL_VELOCITY = std::numbers::pi * 1.f; }// namespace arenai::model diff --git a/arenai_model/src/jolt_engine.cpp b/arenai_model/src/jolt_engine.cpp index 8020c0fa..e01a65b8 100644 --- a/arenai_model/src/jolt_engine.cpp +++ b/arenai_model/src/jolt_engine.cpp @@ -44,8 +44,8 @@ namespace { JPH::uint GetNumBroadPhaseLayers() const override { return broad_phase_layers::NUM_LAYERS; } JPH::BroadPhaseLayer GetBroadPhaseLayer(const JPH::ObjectLayer layer) const override { - return layer == arenai::model::layers::NON_MOVING ? broad_phase_layers::NON_MOVING - : broad_phase_layers::MOVING; + return layer == layers::NON_MOVING ? broad_phase_layers::NON_MOVING + : broad_phase_layers::MOVING; } #if defined(JPH_EXTERNAL_PROFILE) || defined(JPH_PROFILE_ENABLED) @@ -59,8 +59,7 @@ namespace { public: bool ShouldCollide( const JPH::ObjectLayer layer1, const JPH::BroadPhaseLayer layer2) const override { - return layer1 != arenai::model::layers::NON_MOVING - || layer2 != broad_phase_layers::NON_MOVING; + return layer1 != layers::NON_MOVING || layer2 != broad_phase_layers::NON_MOVING; } }; @@ -68,8 +67,7 @@ namespace { public: bool ShouldCollide(const JPH::ObjectLayer layer1, const JPH::ObjectLayer layer2) const override { - return layer1 != arenai::model::layers::NON_MOVING - || layer2 != arenai::model::layers::NON_MOVING; + return layer1 != layers::NON_MOVING || layer2 != layers::NON_MOVING; } }; diff --git a/arenai_model/src/tank/jolt_tank.cpp b/arenai_model/src/tank/jolt_tank.cpp index 268c121c..84673784 100644 --- a/arenai_model/src/tank/jolt_tank.cpp +++ b/arenai_model/src/tank/jolt_tank.cpp @@ -18,8 +18,6 @@ using namespace arenai; using namespace arenai::model; -using namespace arenai::view; -using namespace arenai::controller; namespace arenai::model { @@ -157,11 +155,13 @@ namespace arenai::model { engine.add_jolt_item_producer([c = canon_item] { return c->produce_jolt_items(); }); } - std::shared_ptr JoltTank::get_camera() { return camera; } + std::shared_ptr JoltTank::get_camera() { return camera; } std::vector> JoltTank::get_items() { return items; } - std::vector> JoltTank::get_controllers() { return controllers; } + std::vector> JoltTank::get_controllers() { + return controllers; + } std::map> JoltTank::load_shell_shapes() const { return {{ShellItem::NAME, ShellItem::load_shape(file_reader)}}; diff --git a/arenai_model/src/tank/parts/canon.cpp b/arenai_model/src/tank/parts/canon.cpp index 9c14fc8e..0f527c11 100644 --- a/arenai_model/src/tank/parts/canon.cpp +++ b/arenai_model/src/tank/parts/canon.cpp @@ -10,7 +10,6 @@ using namespace arenai; using namespace arenai::model; -using namespace arenai::controller; namespace { @@ -101,7 +100,7 @@ namespace arenai::model { return {jolt_items.begin(), jolt_items.end()}; } - void CanonItem::apply_input(const user_input &input) { + void CanonItem::apply_input(const controller::user_input &input) { angle += input.right_joystick.y * 0.4f; angle = diff --git a/arenai_model/src/tank/parts/turret.cpp b/arenai_model/src/tank/parts/turret.cpp index bd29f561..9f122d0e 100644 --- a/arenai_model/src/tank/parts/turret.cpp +++ b/arenai_model/src/tank/parts/turret.cpp @@ -6,7 +6,6 @@ #include #include -#include using namespace arenai; using namespace arenai::model; diff --git a/arenai_model/src/tank/parts/wheel.cpp b/arenai_model/src/tank/parts/wheel.cpp index 9051835f..3d9d7f89 100644 --- a/arenai_model/src/tank/parts/wheel.cpp +++ b/arenai_model/src/tank/parts/wheel.cpp @@ -4,8 +4,6 @@ #include "./wheel.h" -#include - #include #include diff --git a/arenai_model/tests/include/arenai_model_tests/tests_enemy_tank/tests_enemy_tank.h b/arenai_model/tests/include/arenai_model_tests/tests_enemy_tank/tests_enemy_tank.h index 111e0973..dbd2cd1a 100644 --- a/arenai_model/tests/include/arenai_model_tests/tests_enemy_tank/tests_enemy_tank.h +++ b/arenai_model/tests/include/arenai_model_tests/tests_enemy_tank/tests_enemy_tank.h @@ -5,8 +5,6 @@ #ifndef ARENAI_MODEL_TESTS_ENEMY_TANK_H #define ARENAI_MODEL_TESTS_ENEMY_TANK_H -#include - #include "../utils/engine_test_fixture.h" class EnemyTankTest : public EngineTestFixture {}; diff --git a/arenai_model/tests/include/arenai_model_tests/tests_player_tank/tests_player_tank.h b/arenai_model/tests/include/arenai_model_tests/tests_player_tank/tests_player_tank.h index ce699b16..f34f7b86 100644 --- a/arenai_model/tests/include/arenai_model_tests/tests_player_tank/tests_player_tank.h +++ b/arenai_model/tests/include/arenai_model_tests/tests_player_tank/tests_player_tank.h @@ -5,8 +5,6 @@ #ifndef ARENAI_MODEL_TESTS_PLAYER_TANK_H #define ARENAI_MODEL_TESTS_PLAYER_TANK_H -#include - #include "../utils/engine_test_fixture.h" class PlayerTankTest : public EngineTestFixture {}; diff --git a/arenai_model/tests/include/arenai_model_tests/tests_proprioception/tests_proprioception.h b/arenai_model/tests/include/arenai_model_tests/tests_proprioception/tests_proprioception.h index f900bdef..3d3e7f11 100644 --- a/arenai_model/tests/include/arenai_model_tests/tests_proprioception/tests_proprioception.h +++ b/arenai_model/tests/include/arenai_model_tests/tests_proprioception/tests_proprioception.h @@ -5,8 +5,6 @@ #ifndef ARENAI_MODEL_TESTS_PROPRIOCEPTION_H #define ARENAI_MODEL_TESTS_PROPRIOCEPTION_H -#include - #include "../utils/engine_test_fixture.h" class ProprioceptionTest : public EngineTestFixture {}; diff --git a/arenai_model/tests/include/arenai_model_tests/tests_reward/tests_reward.h b/arenai_model/tests/include/arenai_model_tests/tests_reward/tests_reward.h index 8908d93d..7216b0a2 100644 --- a/arenai_model/tests/include/arenai_model_tests/tests_reward/tests_reward.h +++ b/arenai_model/tests/include/arenai_model_tests/tests_reward/tests_reward.h @@ -5,8 +5,6 @@ #ifndef ARENAI_MODEL_TESTS_REWARD_H #define ARENAI_MODEL_TESTS_REWARD_H -#include - #include "../utils/engine_test_fixture.h" class RewardTest : public EngineTestFixture {}; diff --git a/arenai_model/tests/include/arenai_model_tests/tests_shell/tests_shell.h b/arenai_model/tests/include/arenai_model_tests/tests_shell/tests_shell.h index bd060f33..3242cc7d 100644 --- a/arenai_model/tests/include/arenai_model_tests/tests_shell/tests_shell.h +++ b/arenai_model/tests/include/arenai_model_tests/tests_shell/tests_shell.h @@ -5,8 +5,6 @@ #ifndef ARENAI_MODEL_TESTS_SHELL_H #define ARENAI_MODEL_TESTS_SHELL_H -#include - #include "../utils/engine_test_fixture.h" class ShellTest : public EngineTestFixture {}; diff --git a/arenai_model/tests/include/arenai_model_tests/utils/engine_test_fixture.h b/arenai_model/tests/include/arenai_model_tests/utils/engine_test_fixture.h index ba9389fd..dd107629 100644 --- a/arenai_model/tests/include/arenai_model_tests/utils/engine_test_fixture.h +++ b/arenai_model/tests/include/arenai_model_tests/utils/engine_test_fixture.h @@ -11,7 +11,6 @@ #include #include -#include #include #include diff --git a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp index fe84d4f3..d2998a4d 100644 --- a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp +++ b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp @@ -6,7 +6,6 @@ #include #include -#include #include #include diff --git a/arenai_model/tests/src/tests_item/tests_life_item.cpp b/arenai_model/tests/src/tests_item/tests_life_item.cpp index 973f5487..cdaed829 100644 --- a/arenai_model/tests/src/tests_item/tests_life_item.cpp +++ b/arenai_model/tests/src/tests_item/tests_life_item.cpp @@ -131,7 +131,7 @@ namespace { explicit DummyItem(const std::string &name) : Item(name) {} std::shared_ptr get_shape() override { return nullptr; } - glm::mat4 get_model_matrix() override { return glm::mat4(1.f); } + glm::mat4 get_model_matrix() override { return {1.f}; } glm::vec3 get_linear_velocity() override { return glm::vec3(0.f); } glm::vec3 get_angular_velocity() override { return glm::vec3(0.f); } diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index b1127175..e3f5430d 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -7,7 +7,6 @@ #include #include -#include #include using namespace arenai; From 7d78f8a320106722e60c3fd148488fb6cc79e23f Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 18:02:39 +0200 Subject: [PATCH 31/70] fix warnings --- .../src/agents/sac/sac_hyperparams.cpp | 32 +++++++++---------- .../src/agents/sac/sac_replay_buffer.h | 2 -- arenai_agent/src/agents/step_collector.h | 2 -- arenai_agent/src/core/train_environment.cpp | 6 ++-- arenai_agent/src/distributions/beta_law.cpp | 3 +- arenai_agent/src/main.cpp | 2 +- arenai_agent/src/metrics/last_metric.cpp | 2 +- arenai_agent/src/metrics/mean_metric.cpp | 2 ++ arenai_agent/src/metrics/metric.cpp | 2 +- arenai_agent/src/metrics/metric.h | 3 +- arenai_agent/src/metrics/std_metric.cpp | 3 ++ arenai_agent/src/networks/actor.cpp | 4 +-- arenai_agent/src/networks/misc.cpp | 6 ++-- arenai_agent/src/networks/misc.h | 25 +++++++++------ .../src/networks_utils/torch_loader.h | 2 +- arenai_agent/src/train.cpp | 1 - arenai_agent/src/utils/image_write.cpp | 2 ++ arenai_agent/src/utils/image_writer.h | 2 -- .../tests_agents/tests_sac_training.h | 1 - .../tests_truncated_normal.h | 4 +-- .../tests_e2e/interceptor_agent.h | 13 +++++--- .../tests_networks/tests_actor.h | 10 +++--- .../tests_networks/tests_q_function.h | 12 +++---- .../tests_networks/tests_vision.h | 8 ++--- .../tests/src/tests_agents/tests_ppo.cpp | 19 ++++++++--- .../tests_agents/tests_ppo_rollout_buffer.cpp | 4 +-- .../src/tests_agents/tests_ppo_training.cpp | 9 +++--- .../src/tests_agents/tests_sac_training.cpp | 19 ++++++++--- .../tests_distributions/tests_beta_law.cpp | 10 +++--- .../tests_gaussian_tanh.cpp | 8 ++--- .../tests_gaussian_tanh_edge.cpp | 6 ++-- .../tests_truncated_normal_edge.cpp | 8 ++--- .../tests_metrics/tests_abstract_metric.cpp | 10 +++--- .../src/tests_metrics/tests_metrics_edge.cpp | 1 + .../src/tests_metrics/tests_std_metric.cpp | 2 ++ .../tests/src/tests_networks/tests_misc.cpp | 4 +-- .../tests_q_function_consistency.cpp | 2 +- .../src/tests_networks/tests_vision_edge.cpp | 2 +- .../src/tests_networks_utils/tests_init.cpp | 3 +- .../create_random_step.cpp | 19 +++++------ .../tests_replay_buffer_edge.cpp | 4 +-- .../src/tests_utils/tests_file_reader.cpp | 16 +++++----- .../src/tests_utils/tests_torch_converter.cpp | 12 +++---- 43 files changed, 172 insertions(+), 135 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_hyperparams.cpp b/arenai_agent/src/agents/sac/sac_hyperparams.cpp index d1a4fbab..70657322 100644 --- a/arenai_agent/src/agents/sac/sac_hyperparams.cpp +++ b/arenai_agent/src/agents/sac/sac_hyperparams.cpp @@ -8,22 +8,22 @@ namespace arenai::agent { std::vector> sac_cli_fields() { return { - {"--actor_learning_rate", &SacHyperParams::actor_learning_rate}, - {"--critic_learning_rate", &SacHyperParams::critic_learning_rate}, - {"--alpha_learning_rate", &SacHyperParams::alpha_learning_rate}, - {"--hidden_size_sensors", &SacHyperParams::hidden_size_sensors}, - {"--hidden_size_actions", &SacHyperParams::hidden_size_actions}, - {"--actor_hidden_sizes", &SacHyperParams::actor_hidden_sizes}, - {"--critic_hidden_sizes", &SacHyperParams::critic_hidden_sizes}, - {"--vision_channels", &SacHyperParams::vision_channels}, - {"--group_norm_nums", &SacHyperParams::group_norm_nums}, - {"--metric_window_size", &SacHyperParams::metric_window_size}, - {"--tau", &SacHyperParams::tau}, - {"--gamma", &SacHyperParams::gamma}, - {"--replay_buffer_size", &SacHyperParams::replay_buffer_size}, - {"--train_every", &SacHyperParams::train_every}, - {"--epochs", &SacHyperParams::epochs}, - {"--batch_size", &SacHyperParams::batch_size}, + {.name = "--actor_learning_rate", .member = &SacHyperParams::actor_learning_rate}, + {.name = "--critic_learning_rate", .member = &SacHyperParams::critic_learning_rate}, + {.name = "--alpha_learning_rate", .member = &SacHyperParams::alpha_learning_rate}, + {.name = "--hidden_size_sensors", .member = &SacHyperParams::hidden_size_sensors}, + {.name = "--hidden_size_actions", .member = &SacHyperParams::hidden_size_actions}, + {.name = "--actor_hidden_sizes", .member = &SacHyperParams::actor_hidden_sizes}, + {.name = "--critic_hidden_sizes", .member = &SacHyperParams::critic_hidden_sizes}, + {.name = "--vision_channels", .member = &SacHyperParams::vision_channels}, + {.name = "--group_norm_nums", .member = &SacHyperParams::group_norm_nums}, + {.name = "--metric_window_size", .member = &SacHyperParams::metric_window_size}, + {.name = "--tau", .member = &SacHyperParams::tau}, + {.name = "--gamma", .member = &SacHyperParams::gamma}, + {.name = "--replay_buffer_size", .member = &SacHyperParams::replay_buffer_size}, + {.name = "--train_every", .member = &SacHyperParams::train_every}, + {.name = "--epochs", .member = &SacHyperParams::epochs}, + {.name = "--batch_size", .member = &SacHyperParams::batch_size}, }; } diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.h b/arenai_agent/src/agents/sac/sac_replay_buffer.h index 185cdbbd..48907149 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.h +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.h @@ -5,8 +5,6 @@ #ifndef ARENAI_AGENT_HOST_REPLAY_BUFFER_H #define ARENAI_AGENT_HOST_REPLAY_BUFFER_H -#include - #include #include "../torch_types.h" diff --git a/arenai_agent/src/agents/step_collector.h b/arenai_agent/src/agents/step_collector.h index ac4913cb..9ab8975e 100644 --- a/arenai_agent/src/agents/step_collector.h +++ b/arenai_agent/src/agents/step_collector.h @@ -5,8 +5,6 @@ #ifndef ARENAI_STEP_COLLECTOR_H #define ARENAI_STEP_COLLECTOR_H -#include - #include "./torch_types.h" namespace arenai::agent { diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index 6bb2a6f1..765a1231 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -4,6 +4,8 @@ #include "./train_environment.h" +#include + #include #include #include @@ -22,7 +24,7 @@ namespace arenai::agent { const std::filesystem::path &android_assets_path, const float wanted_frequency, const int max_episode_steps, const int vision_height, const int vision_width, const int vision_num_threads) - : core::BaseTanksEnvironment( + : BaseTanksEnvironment( std::make_shared(android_assets_path), graphics_backend, nb_tanks, wanted_frequency, vision_height, vision_width, vision_num_threads, false), wanted_frequency(wanted_frequency), @@ -46,7 +48,7 @@ namespace arenai::agent { // mark them so the caller can skip their post-mortem steps already_done = done; - auto step_result = core::BaseTanksEnvironment::step(time_delta, actions); + auto step_result = BaseTanksEnvironment::step(time_delta, actions); const auto has_hit = apply_on_factories>([&](const auto &factories) { std::vector has_hit_result; diff --git a/arenai_agent/src/distributions/beta_law.cpp b/arenai_agent/src/distributions/beta_law.cpp index 738b179b..9fce01e9 100644 --- a/arenai_agent/src/distributions/beta_law.cpp +++ b/arenai_agent/src/distributions/beta_law.cpp @@ -61,7 +61,8 @@ namespace arenai::agent { } float beta_law_target_entropy(const int &nb_actions) { - return beta_law_entropy(torch::tensor(1.f), torch::tensor(1.f)).item() * nb_actions; + return beta_law_entropy(torch::tensor(1.f), torch::tensor(1.f)).item() + * static_cast(nb_actions); } }// namespace arenai::agent diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 88ae26c3..9ae9db50 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -43,7 +43,7 @@ int main(const int argc, char **argv) { .default_value(static_cast(std::thread::hardware_concurrency())); // one subcommand per algorithm, carrying its own hyper-parameter options - for (const auto &algorithm: algorithms) parser.add_subparser(*algorithm.parser); + for (const auto &[name, subparser, create_agent]: algorithms) parser.add_subparser(*subparser); parser.parse_args(argc, argv); diff --git a/arenai_agent/src/metrics/last_metric.cpp b/arenai_agent/src/metrics/last_metric.cpp index 65469dd0..d9628484 100644 --- a/arenai_agent/src/metrics/last_metric.cpp +++ b/arenai_agent/src/metrics/last_metric.cpp @@ -11,5 +11,5 @@ LastMetric::LastMetric(const std::string &name, const int precision, const bool : AbstractMetric(name, 1, precision, scientific) {} float LastMetric::compute_metric_impl(const std::vector &curr_values) { - return curr_values.size() > 0 ? curr_values.back() : 0.f; + return curr_values.empty() ? curr_values.back() : 0.f; } diff --git a/arenai_agent/src/metrics/mean_metric.cpp b/arenai_agent/src/metrics/mean_metric.cpp index 54fcbd77..7910ce27 100644 --- a/arenai_agent/src/metrics/mean_metric.cpp +++ b/arenai_agent/src/metrics/mean_metric.cpp @@ -4,6 +4,8 @@ #include "./mean_metric.h" +#include + using namespace arenai; using namespace arenai::agent; diff --git a/arenai_agent/src/metrics/metric.cpp b/arenai_agent/src/metrics/metric.cpp index f0b32464..fdbae9b3 100644 --- a/arenai_agent/src/metrics/metric.cpp +++ b/arenai_agent/src/metrics/metric.cpp @@ -4,7 +4,7 @@ #include "./metric.h" -#include +#include #include using namespace arenai; diff --git a/arenai_agent/src/metrics/metric.h b/arenai_agent/src/metrics/metric.h index 700ac281..140421b6 100644 --- a/arenai_agent/src/metrics/metric.h +++ b/arenai_agent/src/metrics/metric.h @@ -5,11 +5,10 @@ #ifndef ARENAI_AGENT_HOST_METRIC_H #define ARENAI_AGENT_HOST_METRIC_H +#include #include #include -#include - namespace arenai::agent { class AbstractMetric { diff --git a/arenai_agent/src/metrics/std_metric.cpp b/arenai_agent/src/metrics/std_metric.cpp index 7d77f1cb..84d1e2ba 100644 --- a/arenai_agent/src/metrics/std_metric.cpp +++ b/arenai_agent/src/metrics/std_metric.cpp @@ -4,6 +4,9 @@ #include "./std_metric.h" +#include +#include + using namespace arenai; using namespace arenai::agent; diff --git a/arenai_agent/src/networks/actor.cpp b/arenai_agent/src/networks/actor.cpp index 03113aac..d0269755 100644 --- a/arenai_agent/src/networks/actor.cpp +++ b/arenai_agent/src/networks/actor.cpp @@ -64,9 +64,9 @@ namespace arenai::agent { head->apply(init_hidden_weights); mu->apply(init_mu_output_weights); - sigma->apply([](torch::nn::Module &m) { init_sigma_output_weights(m); }); + sigma->apply([](Module &m) { init_sigma_output_weights(m); }); - discrete->apply([](torch::nn::Module &m) { init_discrete_output_weights(m); }); + discrete->apply([](Module &m) { init_discrete_output_weights(m); }); } ActorRawOutput Actor::act(const torch::Tensor &vision, const torch::Tensor &sensors) { diff --git a/arenai_agent/src/networks/misc.cpp b/arenai_agent/src/networks/misc.cpp index 1c276cca..929aec17 100644 --- a/arenai_agent/src/networks/misc.cpp +++ b/arenai_agent/src/networks/misc.cpp @@ -15,7 +15,7 @@ namespace arenai::agent { torch::Tensor Exp::forward(const torch::Tensor &x) { return torch::exp(x); } - void Exp::pretty_print(std::ostream &stream) const { stream << name() << "()"; } + void Exp::pretty_print(std::ostream &stream) { stream << name() << "()"; } /* * Clamp @@ -28,7 +28,7 @@ namespace arenai::agent { return torch::clamp(x, lower_bound, upper_bound); } - void Clamp::pretty_print(std::ostream &stream) const { + void Clamp::pretty_print(std::ostream &stream) { stream << name() << "(min=" << lower_bound << ", max=" << upper_bound << ")"; } @@ -45,7 +45,7 @@ namespace arenai::agent { return torch::exp(log_sigma); } - void SigmaOutput::pretty_print(std::ostream &stream) const { + void SigmaOutput::pretty_print(std::ostream &stream) { stream << name() << "(min=" << std::exp(min_log_sigma) << ", max=" << std::exp(max_log_sigma) << ")"; } diff --git a/arenai_agent/src/networks/misc.h b/arenai_agent/src/networks/misc.h index f28bc258..70daf773 100644 --- a/arenai_agent/src/networks/misc.h +++ b/arenai_agent/src/networks/misc.h @@ -9,33 +9,40 @@ namespace arenai::agent { - class Clamp : public torch::nn::Module { + class AbstractFunctionModule : public torch::nn::Module { + public: + virtual torch::Tensor forward(const torch::Tensor &input) = 0; + + virtual void pretty_print(std::ostream &stream) = 0; + }; + + class Clamp : public AbstractFunctionModule { public: Clamp(float lower_bound, float upper_bound); - torch::Tensor forward(const torch::Tensor &x); + torch::Tensor forward(const torch::Tensor &x) override; - void pretty_print(std::ostream &stream) const override; + void pretty_print(std::ostream &stream) override; private: float lower_bound; float upper_bound; }; - class Exp : public torch::nn::Module { + class Exp : public AbstractFunctionModule { public: - torch::Tensor forward(const torch::Tensor &x); + torch::Tensor forward(const torch::Tensor &x) override; - void pretty_print(std::ostream &stream) const override; + void pretty_print(std::ostream &stream) override; }; - class SigmaOutput : public torch::nn::Module { + class SigmaOutput : public AbstractFunctionModule { public: SigmaOutput(float min_sigma, float max_sigma); - torch::Tensor forward(const torch::Tensor &input); + torch::Tensor forward(const torch::Tensor &input) override; - void pretty_print(std::ostream &stream) const override; + void pretty_print(std::ostream &stream) override; private: float min_log_sigma; diff --git a/arenai_agent/src/networks_utils/torch_loader.h b/arenai_agent/src/networks_utils/torch_loader.h index 6ad13577..3ad16973 100644 --- a/arenai_agent/src/networks_utils/torch_loader.h +++ b/arenai_agent/src/networks_utils/torch_loader.h @@ -26,7 +26,7 @@ namespace arenai::agent { torch::serialize::InputArchive archive; archive.load_from(file.string(), torch::kCPU); to_load->load(archive); - } catch (const std::exception &e) { + } catch (const std::exception &_) { std::throw_with_nested(utils::ModelLoadException(file)); } } diff --git a/arenai_agent/src/train.cpp b/arenai_agent/src/train.cpp index 4e4bafa8..a911b650 100644 --- a/arenai_agent/src/train.cpp +++ b/arenai_agent/src/train.cpp @@ -12,7 +12,6 @@ #include #include "./core/train_environment.h" -#include "./metrics/mean_metric.h" #include "./metrics/metric_saver.h" #include "./networks_utils/torch_converter.h" #include "./networks_utils/torch_saver.h" diff --git a/arenai_agent/src/utils/image_write.cpp b/arenai_agent/src/utils/image_write.cpp index 6e1a0b5d..0a0f1b03 100644 --- a/arenai_agent/src/utils/image_write.cpp +++ b/arenai_agent/src/utils/image_write.cpp @@ -8,6 +8,8 @@ #define STB_IMAGE_WRITE_STATIC #define STB_IMAGE_WRITE_IMPLEMENTATION +#include + #include "./image_writer.h" using namespace arenai; diff --git a/arenai_agent/src/utils/image_writer.h b/arenai_agent/src/utils/image_writer.h index 16b8062e..b854fecd 100644 --- a/arenai_agent/src/utils/image_writer.h +++ b/arenai_agent/src/utils/image_writer.h @@ -5,10 +5,8 @@ #ifndef ARENAI_AGENT_HOST_IMAGE_WRITER_H #define ARENAI_AGENT_HOST_IMAGE_WRITER_H -#include #include -#include #include namespace arenai::agent { diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_sac_training.h b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_sac_training.h index cd8bdc0e..4f36fd38 100644 --- a/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_sac_training.h +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_agents/tests_sac_training.h @@ -5,7 +5,6 @@ #ifndef ARENAI_TESTS_SAC_TRAINING_H #define ARENAI_TESTS_SAC_TRAINING_H -#include #include #include diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_distributions/tests_truncated_normal.h b/arenai_agent/tests/include/arenai_agent_tests/tests_distributions/tests_truncated_normal.h index 7e675690..af280dbb 100644 --- a/arenai_agent/tests/include/arenai_agent_tests/tests_distributions/tests_truncated_normal.h +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_distributions/tests_truncated_normal.h @@ -7,8 +7,8 @@ #include -typedef int UpperBound; -typedef int LowerBound; +typedef float UpperBound; +typedef float LowerBound; typedef std::vector Shape; diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_e2e/interceptor_agent.h b/arenai_agent/tests/include/arenai_agent_tests/tests_e2e/interceptor_agent.h index 9f026908..d7a72b60 100644 --- a/arenai_agent/tests/include/arenai_agent_tests/tests_e2e/interceptor_agent.h +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_e2e/interceptor_agent.h @@ -12,20 +12,23 @@ // Records every batch of states the host loop hands to the agent and answers // with neutral actions: what act() received is exactly what a real network // would have seen. -class InterceptorAgent final : public arenai::agent::AbstractAgent { +class InterceptorAgent final : public agent::AbstractAgent { public: - std::vector> received_states; + std::vector> received_states; int last_vision_height = -1; int last_vision_width = -1; - std::vector - act(const std::vector &states, const int vision_height, + std::vector + act(const std::vector &states, const int vision_height, const int vision_width) override { received_states.push_back(states); last_vision_height = vision_height; last_vision_width = vision_width; - return std::vector(states.size(), {{0.f, 0.f}, {0.f, 0.f}, {false}}); + return std::vector( + states.size(), {.left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {false}}); } void load(const std::filesystem::path &agent_folder) override {} diff --git a/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_actor.h b/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_actor.h index 26a253df..4e658e18 100644 --- a/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_actor.h +++ b/arenai_agent/tests/include/arenai_agent_tests/tests_networks/tests_actor.h @@ -8,13 +8,13 @@ #include typedef std::vector HiddenLayers; -typedef uint32_t ContinuousActionsNb; -typedef uint32_t DiscreteActionsNb; +typedef int ContinuousActionsNb; +typedef int DiscreteActionsNb; -typedef uint32_t SensorsNb; -typedef uint32_t SensorsHiddenSize; +typedef int SensorsNb; +typedef int SensorsHiddenSize; -typedef uint32_t BatchSize; +typedef int BatchSize; class ActorTestParam : public testing::TestWithParam typedef std::vector HiddenLayers; -typedef uint32_t ContinuousActionsNb; -typedef uint32_t DiscreteActionsNb; +typedef int ContinuousActionsNb; +typedef int DiscreteActionsNb; -typedef uint32_t SensorsNb; -typedef uint32_t SensorsHiddenSize; +typedef int SensorsNb; +typedef int SensorsHiddenSize; -typedef uint32_t ActionsHiddenSize; +typedef int ActionsHiddenSize; -typedef uint32_t BatchSize; +typedef int BatchSize; class QFunctionTestParam : public testing::TestWithParam -typedef uint32_t VisionWidth; -typedef uint32_t VisionHeight; -typedef uint32_t VisionChannel; +typedef int VisionWidth; +typedef int VisionHeight; +typedef int VisionChannel; typedef std::vector OutputConvChannels; typedef std::vector GroupNormNums; -typedef uint32_t BatchSize; +typedef int BatchSize; class VisionTestParam : public testing::TestWithParam PpoAgentTest::make_factory(const PpoTestCo TorchState PpoAgentTest::make_state(const PpoTestConfig &cfg, const int batch) { return { - torch::randint(0, 255, {batch, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), - torch::randn({batch, cfg.nb_sensors})}; + .vision = + torch::randint(0, 255, {batch, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), + .proprioception = torch::randn({batch, cfg.nb_sensors})}; } // ======================================================================== @@ -53,14 +54,24 @@ TorchState PpoAgentTest::make_state(const PpoTestConfig &cfg, const int batch) { // ======================================================================== TEST_F(PpoAgentTest, ParameterCountPositive) { - constexpr PpoTestConfig cfg{8, 8, 10, 4, 2}; + constexpr PpoTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 10, + .nb_continuous_actions = 4, + .nb_discrete_actions = 2}; const auto factory = make_factory(cfg); ASSERT_GT(factory->get_trainer()->count_parameters(), 0); } TEST_F(PpoAgentTest, MetricsNotEmpty) { - constexpr PpoTestConfig cfg{8, 8, 10, 4, 2}; + constexpr PpoTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 10, + .nb_continuous_actions = 4, + .nb_discrete_actions = 2}; const auto factory = make_factory(cfg); const auto metrics = factory->get_trainer()->get_metrics(); diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp index 02bdbfcc..e4029a07 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_rollout_buffer.cpp @@ -13,8 +13,8 @@ using namespace arenai::agent; TorchState PpoRolloutBufferTest::make_state() { return { - torch::randn({NB_TANKS, 3, VISION_SIZE, VISION_SIZE}), - torch::randn({NB_TANKS, NB_SENSORS})}; + .vision = torch::randn({NB_TANKS, 3, VISION_SIZE, VISION_SIZE}), + .proprioception = torch::randn({NB_TANKS, NB_SENSORS})}; } PpoInputStep PpoRolloutBufferTest::make_step(const TorchState &state, const torch::Tensor &done) { diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index 86c0361a..e7e210c0 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -63,21 +63,20 @@ TEST_F(PpoTrainingTest, CountParametersPositive) { TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { // build the triad by hand to keep a handle on the actor's parameters constexpr PpoTrainingTestConfig cfg{8, 8, 3, 2, 3}; - constexpr int nb_tanks = 2; const std::vector> vision_channels{{3, 4}}; - const std::vector group_norm_nums{2}; + const std::vector group_norm_nums{2}; const auto actor = std::make_shared( cfg.vision_height, cfg.vision_width, cfg.nb_sensors, cfg.nb_continuous_actions, - cfg.nb_discrete_actions, 8, std::vector{16}, vision_channels, group_norm_nums); + cfg.nb_discrete_actions, 8, std::vector{16}, vision_channels, group_norm_nums); const auto rollout_buffer = std::make_shared(); const auto collector = std::make_shared(rollout_buffer); const auto agent = std::make_shared(actor, device, collector); // target_kl = 0 : early stop disabled so every minibatch applies its update const auto trainer = std::make_shared( actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, - cfg.nb_continuous_actions, 1e-3f, 1e-3f, 1e-3f, 8, std::vector{16}, vision_channels, + cfg.nb_continuous_actions, 1e-3f, 1e-3f, 1e-3f, 8, std::vector{16}, vision_channels, group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, 1.f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE); std::vector initial_parameters; @@ -87,6 +86,8 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { // env loop: act -> transition -> maybe train, one more step than the rollout // horizon so that the batch is complete when the trainer checks for (int t = 0; t < ROLLOUT_SIZE + 2; t++) { + constexpr int nb_tanks = 2; + agent->act(make_state(cfg, nb_tanks)); collector->on_transition(torch::randn({nb_tanks, 1}), torch::zeros({nb_tanks, 1})); trainer->step(); diff --git a/arenai_agent/tests/src/tests_agents/tests_sac_training.cpp b/arenai_agent/tests/src/tests_agents/tests_sac_training.cpp index e4feb314..5474d0eb 100644 --- a/arenai_agent/tests/src/tests_agents/tests_sac_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_sac_training.cpp @@ -34,12 +34,18 @@ SacTrainingTest::make_factory(const SacTrainingTestConfig &cfg) const { TorchState SacTrainingTest::make_state(const SacTrainingTestConfig &cfg) { return { - torch::randint(0, 255, {1, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), - torch::randn({1, cfg.nb_sensors})}; + .vision = + torch::randint(0, 255, {1, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), + .proprioception = torch::randn({1, cfg.nb_sensors})}; } TEST_F(SacTrainingTest, ActProducesValidOutput) { - constexpr SacTrainingTestConfig cfg{8, 8, 3, 2, 3}; + constexpr SacTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; const auto factory = make_factory(cfg); const auto [continuous_action, discrete_action] = factory->get_agent()->act(make_state(cfg)); @@ -54,7 +60,12 @@ TEST_F(SacTrainingTest, ActProducesValidOutput) { } TEST_F(SacTrainingTest, CountParametersPositive) { - constexpr SacTrainingTestConfig cfg{8, 8, 3, 2, 3}; + constexpr SacTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; const auto factory = make_factory(cfg); ASSERT_GT(factory->get_trainer()->count_parameters(), 0) diff --git a/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp b/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp index 0085ac0c..bb534ecc 100644 --- a/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp +++ b/arenai_agent/tests/src/tests_distributions/tests_beta_law.cpp @@ -43,7 +43,7 @@ TEST_F(BetaLawTest, LogProbaConsistentWithSample) { // ======================================================================== TEST_P(BetaLawParamTest, SampleBounds) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto alpha = torch::rand(shape) * 4.0f + 0.5f; const auto beta = torch::rand(shape) * 4.0f + 0.5f; @@ -56,7 +56,7 @@ TEST_P(BetaLawParamTest, SampleBounds) { } TEST_P(BetaLawParamTest, LogProbaShape) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto alpha = torch::rand(shape) * 4.0f + 0.5f; const auto beta = torch::rand(shape) * 4.0f + 0.5f; @@ -69,7 +69,7 @@ TEST_P(BetaLawParamTest, LogProbaShape) { } TEST_P(BetaLawParamTest, EntropyShape) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto alpha = torch::rand(shape) * 4.0f + 0.5f; const auto beta = torch::rand(shape) * 4.0f + 0.5f; @@ -81,7 +81,7 @@ TEST_P(BetaLawParamTest, EntropyShape) { } TEST_P(BetaLawParamTest, SampleNoNaNWithSmallParams) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto alpha = torch::ones(shape) * 0.1f; const auto beta = torch::ones(shape) * 0.1f; @@ -94,7 +94,7 @@ TEST_P(BetaLawParamTest, SampleNoNaNWithSmallParams) { } TEST_P(BetaLawParamTest, SampleNoNaNWithLargeParams) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto alpha = torch::ones(shape) * 50.0f; const auto beta = torch::ones(shape) * 50.0f; diff --git a/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh.cpp b/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh.cpp index f2967ecf..a6b4b9fb 100644 --- a/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh.cpp +++ b/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh.cpp @@ -52,7 +52,7 @@ TEST_F(GaussianTanhTest, SmallSigmaConcentratesAction) { // ======================================================================== TEST_P(GaussianTanhShapeParamTest, SampleShapeAndBounds) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto mu = torch::randn(shape); const auto sigma = torch::rand(shape) + 0.1f; @@ -66,7 +66,7 @@ TEST_P(GaussianTanhShapeParamTest, SampleShapeAndBounds) { } TEST_P(GaussianTanhShapeParamTest, LogPdfShapeAndFinite) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto mu = torch::randn(shape); const auto sigma = torch::rand(shape) + 0.1f; @@ -79,7 +79,7 @@ TEST_P(GaussianTanhShapeParamTest, LogPdfShapeAndFinite) { } TEST_P(GaussianTanhShapeParamTest, SampleFiniteWithZeroMu) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto mu = torch::zeros(shape); const auto sigma = torch::ones(shape) * 0.5f; @@ -91,7 +91,7 @@ TEST_P(GaussianTanhShapeParamTest, SampleFiniteWithZeroMu) { } TEST_P(GaussianTanhShapeParamTest, SampleFiniteWithLargeMu) { - const auto shape = GetParam(); + const auto &shape = GetParam(); const auto mu = torch::ones(shape) * 10.0f; const auto sigma = torch::ones(shape) * 0.1f; diff --git a/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh_edge.cpp b/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh_edge.cpp index 5427a9d4..15de67e3 100644 --- a/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh_edge.cpp +++ b/arenai_agent/tests/src/tests_distributions/tests_gaussian_tanh_edge.cpp @@ -85,7 +85,7 @@ TEST_F(GaussianTanhEdgeTest, SampleWithZeroSigma) { // ======================================================================== TEST_F(GaussianTanhGradientTest, LogPdfGradientFlowsThroughMu) { - auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); + const auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); const auto sigma = torch::ones({5}) * 0.5f; const auto u = torch::tensor({0.1f, -0.2f, 0.3f, -0.1f, 0.0f}); @@ -103,7 +103,7 @@ TEST_F(GaussianTanhGradientTest, LogPdfGradientFlowsThroughMu) { TEST_F(GaussianTanhGradientTest, LogPdfGradientFlowsThroughSigma) { const auto mu = torch::zeros({5}); - auto sigma = torch::full({5}, 0.5f, torch::TensorOptions().requires_grad(true)); + const auto sigma = torch::full({5}, 0.5f, torch::TensorOptions().requires_grad(true)); const auto u = torch::tensor({0.1f, -0.2f, 0.3f, -0.1f, 0.0f}); const auto log_p = gaussian_tanh_log_pdf(u, mu, sigma); @@ -117,7 +117,7 @@ TEST_F(GaussianTanhGradientTest, LogPdfGradientFlowsThroughSigma) { } TEST_F(GaussianTanhGradientTest, LogPdfGradientFiniteWithLargeU) { - auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); + const auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); const auto sigma = torch::ones({5}) * 0.5f; const auto u = torch::ones({5}) * 10.0f; diff --git a/arenai_agent/tests/src/tests_distributions/tests_truncated_normal_edge.cpp b/arenai_agent/tests/src/tests_distributions/tests_truncated_normal_edge.cpp index 377e43c1..c348b690 100644 --- a/arenai_agent/tests/src/tests_distributions/tests_truncated_normal_edge.cpp +++ b/arenai_agent/tests/src/tests_distributions/tests_truncated_normal_edge.cpp @@ -71,7 +71,7 @@ TEST_F(TruncatedNormalEdgeTest, LogPdfAndPdfConsistent) { // ======================================================================== TEST_F(TruncatedNormalGradientTest, LogPdfGradientFlowsThroughMu) { - auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); + const auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); const auto sigma = torch::ones({5}) * 0.5f; const auto x = torch::tensor({0.1f, -0.2f, 0.3f, -0.1f, 0.0f}); @@ -89,7 +89,7 @@ TEST_F(TruncatedNormalGradientTest, LogPdfGradientFlowsThroughMu) { TEST_F(TruncatedNormalGradientTest, LogPdfGradientFlowsThroughSigma) { const auto mu = torch::zeros({5}); - auto sigma = torch::full({5}, 0.5f, torch::TensorOptions().requires_grad(true)); + const auto sigma = torch::full({5}, 0.5f, torch::TensorOptions().requires_grad(true)); const auto x = torch::tensor({0.1f, -0.2f, 0.3f, -0.1f, 0.0f}); const auto log_pdf = truncated_normal_log_pdf(x, mu, sigma); @@ -103,7 +103,7 @@ TEST_F(TruncatedNormalGradientTest, LogPdfGradientFlowsThroughSigma) { } TEST_F(TruncatedNormalGradientTest, EntropyGradientFlowsThroughMu) { - auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); + const auto mu = torch::zeros({5}, torch::TensorOptions().requires_grad(true)); const auto sigma = torch::ones({5}) * 0.5f; const auto entropy = truncated_normal_entropy(mu, sigma); @@ -118,7 +118,7 @@ TEST_F(TruncatedNormalGradientTest, EntropyGradientFlowsThroughMu) { TEST_F(TruncatedNormalGradientTest, EntropyGradientFlowsThroughSigma) { const auto mu = torch::zeros({5}); - auto sigma = torch::full({5}, 0.5f, torch::TensorOptions().requires_grad(true)); + const auto sigma = torch::full({5}, 0.5f, torch::TensorOptions().requires_grad(true)); const auto entropy = truncated_normal_entropy(mu, sigma); const auto loss = entropy.sum(); diff --git a/arenai_agent/tests/src/tests_metrics/tests_abstract_metric.cpp b/arenai_agent/tests/src/tests_metrics/tests_abstract_metric.cpp index 2f9e2a32..a44f067d 100644 --- a/arenai_agent/tests/src/tests_metrics/tests_abstract_metric.cpp +++ b/arenai_agent/tests/src/tests_metrics/tests_abstract_metric.cpp @@ -39,7 +39,7 @@ TEST_F(AbstractMetricTest, ToStringFormat) { const auto str = metric.to_string(); ASSERT_TRUE(str.find("loss") != std::string::npos); - ASSERT_TRUE(str.find("=") != std::string::npos); + ASSERT_TRUE(str.find('=') != std::string::npos); ASSERT_TRUE(str.find("2.00") != std::string::npos); } @@ -50,7 +50,7 @@ TEST_F(AbstractMetricTest, ToStringScientific) { const auto str = metric.to_string(); - ASSERT_TRUE(str.find("e") != std::string::npos || str.find("E") != std::string::npos); + ASSERT_TRUE(str.find('e') != std::string::npos || str.find('E') != std::string::npos); } TEST_F(AbstractMetricTest, MetricsToStringMultiple) { @@ -62,9 +62,9 @@ TEST_F(AbstractMetricTest, MetricsToStringMultiple) { const auto str = AbstractMetric::metrics_to_string({m1, m2}); - ASSERT_TRUE(str.find("a") != std::string::npos); - ASSERT_TRUE(str.find("b") != std::string::npos); - ASSERT_TRUE(str.find(",") != std::string::npos); + ASSERT_TRUE(str.find('a') != std::string::npos); + ASSERT_TRUE(str.find('b') != std::string::npos); + ASSERT_TRUE(str.find(',') != std::string::npos); } // ======================================================================== diff --git a/arenai_agent/tests/src/tests_metrics/tests_metrics_edge.cpp b/arenai_agent/tests/src/tests_metrics/tests_metrics_edge.cpp index af076358..d1d084eb 100644 --- a/arenai_agent/tests/src/tests_metrics/tests_metrics_edge.cpp +++ b/arenai_agent/tests/src/tests_metrics/tests_metrics_edge.cpp @@ -2,6 +2,7 @@ // Created by claude on 01/07/2026. // +#include #include #include diff --git a/arenai_agent/tests/src/tests_metrics/tests_std_metric.cpp b/arenai_agent/tests/src/tests_metrics/tests_std_metric.cpp index 3734e3f9..adf9f8e3 100644 --- a/arenai_agent/tests/src/tests_metrics/tests_std_metric.cpp +++ b/arenai_agent/tests/src/tests_metrics/tests_std_metric.cpp @@ -2,6 +2,8 @@ // Created by samuel on 30/06/2026. // +#include + #include #include diff --git a/arenai_agent/tests/src/tests_networks/tests_misc.cpp b/arenai_agent/tests/src/tests_networks/tests_misc.cpp index a682ea96..315a463b 100644 --- a/arenai_agent/tests/src/tests_networks/tests_misc.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_misc.cpp @@ -50,7 +50,7 @@ INSTANTIATE_TEST_SUITE_P( // ======================================================================== TEST_P(ExpModuleParamTest, OutputAlwaysPositive) { - const auto shape = GetParam(); + const auto &shape = GetParam(); Exp exp_module; @@ -62,7 +62,7 @@ TEST_P(ExpModuleParamTest, OutputAlwaysPositive) { } TEST_P(ExpModuleParamTest, MatchesTorchExp) { - const auto shape = GetParam(); + const auto &shape = GetParam(); Exp exp_module; diff --git a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp index 5773ee41..e5a127f8 100644 --- a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp @@ -53,7 +53,7 @@ TEST_F(QFunctionConsistencyTest, ValueOheOutputFinite) { const auto vision = torch::randint(0, 255, {batch, 3, height, width}, torch::kUInt8); const auto sensors = torch::randn({batch, nb_sensors}); const auto cont_actions = torch::randn({batch, nb_cont}); - auto disc_ohe = torch::zeros({batch, nb_disc}); + const auto disc_ohe = torch::zeros({batch, nb_disc}); disc_ohe.select(1, 0).fill_(1.0f); const auto value = q.value_ohe(vision, sensors, cont_actions, disc_ohe); diff --git a/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp b/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp index 3cf4fc1c..4f9041e5 100644 --- a/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_vision_edge.cpp @@ -32,7 +32,7 @@ TEST_F(VisionEdgeTest, NormalizesToExpectedRange) { TEST_F(VisionEdgeTest, OutputSizeMatchesGetOutputSize) { const std::vector> channels = {{3, 8}, {8, 16}}; - const std::vector gnums = {4, 4}; + const std::vector gnums = {4, 4}; constexpr int h = 16, w = 16; constexpr int batch_size = 2; diff --git a/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp b/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp index 6b2a1fae..e9ca62ee 100644 --- a/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp +++ b/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp @@ -66,8 +66,7 @@ TEST_F(InitWeightsTest, SigmaOutputWeightsOrthogonal) { TEST_F(InitWeightsTest, SigmaOutputIsEqualToWantedOne) { torch::nn::Sequential sequential( - torch::nn::Linear(32, 4), - std::make_shared(agent::SIGMA_MIN, agent::SIGMA_MAX)); + torch::nn::Linear(32, 4), std::make_shared(SIGMA_MIN, SIGMA_MAX)); constexpr float wanted_sigma = 0.5f; diff --git a/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp b/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp index c1e22b73..dd7ceb33 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/create_random_step.cpp @@ -8,19 +8,20 @@ using namespace arenai; using namespace arenai::agent; // single-tank state: every tensor carries a leading nb_tanks dimension of 1 -arenai::agent::TorchState -create_random_state(const int width, const int height, const int nb_sensors) { +TorchState create_random_state(const int width, const int height, const int nb_sensors) { return { - torch::randint(255, {1, 3, height, width}, torch::kUInt8), torch::randn({1, nb_sensors})}; + .vision = torch::randint(255, {1, 3, height, width}, torch::kUInt8), + .proprioception = torch::randn({1, nb_sensors})}; } -arenai::agent::SacInputStep create_random_step( +SacInputStep create_random_step( const int width, const int height, const int nb_cont_actions, const int nb_discrete_actions, const int nb_sensors, const bool done) { return { - create_random_state(width, height, nb_sensors), - {torch::rand({1, nb_cont_actions}) * 2.f - 1.f, - torch::softmax(torch::randn({1, nb_discrete_actions}), -1)}, - torch::randn({1, 1}), - torch::full({1, 1}, done, torch::kBool)}; + .state = create_random_state(width, height, nb_sensors), + .action = + {.continuous_action = torch::rand({1, nb_cont_actions}) * 2.f - 1.f, + .discrete_action = torch::softmax(torch::randn({1, nb_discrete_actions}), -1)}, + .reward = torch::randn({1, 1}), + .done = torch::full({1, 1}, done, torch::kBool)}; } diff --git a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp index fe3f728e..93368a59 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp @@ -14,7 +14,7 @@ using namespace arenai; using namespace arenai::agent; TEST_F(ReplayBufferEdgeTest, SampleFromEmptyBufferDoesNotCrash) { - SacReplayBuffer buffer(10); + const SacReplayBuffer buffer(10); ASSERT_EQ(buffer.size(), 0u); @@ -88,7 +88,7 @@ TEST_F(ReplayBufferEdgeTest, RewardDividedByRunningStdAtSample) { const auto rewards = output.reward.reshape({-1}); for (int64_t i = 0; i < rewards.size(0); i++) { - const float r = rewards[i].item(); + const auto r = rewards[i].item(); ASSERT_TRUE(std::abs(r - 0.0f) < 1e-5f || std::abs(r - 2.0f) < 1e-5f) << "Sampled reward " << r << " should be a stored reward divided by the running std"; } diff --git a/arenai_agent/tests/src/tests_utils/tests_file_reader.cpp b/arenai_agent/tests/src/tests_utils/tests_file_reader.cpp index 6623d74b..a0e8cd98 100644 --- a/arenai_agent/tests/src/tests_utils/tests_file_reader.cpp +++ b/arenai_agent/tests/src/tests_utils/tests_file_reader.cpp @@ -34,7 +34,7 @@ TEST_F(DesktopAssetFileReaderTest, ReadTextReturnsContent) { } TEST_F(DesktopAssetFileReaderTest, ReadTextEmptyFile) { - std::ofstream(tmp_dir / "empty.txt"); + std::ofstream tmp(tmp_dir / "empty.txt"); DesktopAssetFileReader reader(tmp_dir); const auto content = reader.read_text("empty.txt"); @@ -92,17 +92,17 @@ TEST_F(DesktopAssetFileReaderTest, ReadPngValidImage) { << "Failed to create test PNG"; DesktopAssetFileReader reader(tmp_dir); - const auto img = reader.read_png("test.png"); + const auto [width, height, channels, out_pixels] = reader.read_png("test.png"); - ASSERT_EQ(img.width, W); - ASSERT_EQ(img.height, H); - ASSERT_EQ(img.channels, 4); - ASSERT_EQ(img.pixels.size(), static_cast(W * H * 4)); + ASSERT_EQ(width, W); + ASSERT_EQ(height, H); + ASSERT_EQ(channels, 4); + ASSERT_EQ(out_pixels.size(), static_cast(W * H * 4)); } TEST_F(DesktopAssetFileReaderTest, ReadPngPixelsNotAllZero) { constexpr int W = 2, H = 2, C = 4; - std::vector pixels(W * H * C, 200); + const std::vector pixels(W * H * C, 200); const auto png_path = (tmp_dir / "bright.png").string(); SOIL_save_image(png_path.c_str(), SOIL_SAVE_TYPE_PNG, W, H, C, pixels.data()); @@ -128,7 +128,7 @@ TEST_F(DesktopAssetFileReaderTest, ReadPngThrowsOnMissing) { TEST_F(DesktopAssetFileReaderTest, ReadPngAlwaysRGBA) { constexpr int W = 2, H = 2, C = 3; - std::vector pixels(W * H * C, 128); + const std::vector pixels(W * H * C, 128); const auto png_path = (tmp_dir / "rgb.png").string(); SOIL_save_image(png_path.c_str(), SOIL_SAVE_TYPE_PNG, W, H, C, pixels.data()); diff --git a/arenai_agent/tests/src/tests_utils/tests_torch_converter.cpp b/arenai_agent/tests/src/tests_utils/tests_torch_converter.cpp index a052cd2b..97415edb 100644 --- a/arenai_agent/tests/src/tests_utils/tests_torch_converter.cpp +++ b/arenai_agent/tests/src/tests_utils/tests_torch_converter.cpp @@ -178,9 +178,9 @@ TEST_P(StatesToTensorParamTest, OutputShapeCorrect) { const auto [batch, height, width] = GetParam(); std::vector states(batch); - for (auto &s: states) { - s.vision.pixels.resize(3 * height * width, 0); - s.proprioception.resize(model::ENEMY_PROPRIOCEPTION_SIZE, 0.0f); + for (auto &[vision, proprioception]: states) { + vision.pixels.resize(3 * height * width, 0); + proprioception.resize(model::ENEMY_PROPRIOCEPTION_SIZE, 0.0f); } const auto [vision, proprioception] = states_to_tensor(states, height, width); @@ -200,9 +200,9 @@ TEST_P(StatesToTensorParamTest, NoGradient) { const auto [batch, height, width] = GetParam(); std::vector states(batch); - for (auto &s: states) { - s.vision.pixels.resize(3 * height * width, 0); - s.proprioception.resize(model::ENEMY_PROPRIOCEPTION_SIZE, 0.0f); + for (auto &[vision, proprioception]: states) { + vision.pixels.resize(3 * height * width, 0); + proprioception.resize(model::ENEMY_PROPRIOCEPTION_SIZE, 0.0f); } const auto [vision, proprioception] = states_to_tensor(states, height, width); From 24a6bb67d284ea4e1f68ccccf80149d30021fc19 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 18:15:33 +0200 Subject: [PATCH 32/70] fix last metric --- arenai_agent/src/metrics/last_metric.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/src/metrics/last_metric.cpp b/arenai_agent/src/metrics/last_metric.cpp index d9628484..7823e0be 100644 --- a/arenai_agent/src/metrics/last_metric.cpp +++ b/arenai_agent/src/metrics/last_metric.cpp @@ -11,5 +11,5 @@ LastMetric::LastMetric(const std::string &name, const int precision, const bool : AbstractMetric(name, 1, precision, scientific) {} float LastMetric::compute_metric_impl(const std::vector &curr_values) { - return curr_values.empty() ? curr_values.back() : 0.f; + return !curr_values.empty() ? curr_values.back() : 0.f; } From 195193a7974d0f312682bc2255589693b0d9c2f5 Mon Sep 17 00:00:00 2001 From: samuel Date: Thu, 13 Aug 2026 21:55:43 +0200 Subject: [PATCH 33/70] * target sigma from 0.1 to 0.2 * fix warnings --- arenai_agent/src/networks/constants.h | 2 +- arenai_desktop/src/controller/control_kind.h | 2 +- .../src/controller/game_input_router.cpp | 8 ++-- .../src/controller/game_input_router.h | 20 +++++----- arenai_desktop/src/controller/gamepad.cpp | 38 +++++++++++------- arenai_desktop/src/controller/gamepad.h | 2 + .../src/controller/mouse_keyboard.cpp | 25 +++++++----- .../src/core/agent_loading_checker.cpp | 2 +- arenai_desktop/src/core/game_environment.cpp | 4 +- arenai_desktop/src/game.cpp | 12 +----- arenai_desktop/src/gui/rml/adapters.cpp | 3 +- arenai_desktop/src/gui/rml/input.cpp | 7 ++-- arenai_desktop/src/gui/rml/input.h | 2 +- arenai_desktop/src/gui/rml_menu.cpp | 22 ++++++---- .../headless_windowed_backend.h | 40 +++++++++---------- .../arenai_desktop_tests/interceptor_agent.h | 13 +++--- .../tests/src/tests_e2e_agent_input.cpp | 2 +- 17 files changed, 111 insertions(+), 93 deletions(-) diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index b70322f5..99b0bc3f 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -8,7 +8,7 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; - constexpr float TARGET_SIGMA = 0.1f; + constexpr float TARGET_SIGMA = 0.2f; constexpr float TARGET_FIRE_PROBABILITY = 0.05f; constexpr float SIGMA_MIN = 1e-4f; diff --git a/arenai_desktop/src/controller/control_kind.h b/arenai_desktop/src/controller/control_kind.h index 3ea9a972..e083aaa6 100644 --- a/arenai_desktop/src/controller/control_kind.h +++ b/arenai_desktop/src/controller/control_kind.h @@ -7,6 +7,6 @@ namespace arenai::desktop { enum class ControllerKind { Keyboard, Gamepad }; -}; +} #endif//ARENAI_CONTROL_KIND_H diff --git a/arenai_desktop/src/controller/game_input_router.cpp b/arenai_desktop/src/controller/game_input_router.cpp index f138fd6c..2155d01b 100644 --- a/arenai_desktop/src/controller/game_input_router.cpp +++ b/arenai_desktop/src/controller/game_input_router.cpp @@ -9,10 +9,10 @@ namespace arenai::desktop { GameInputRouter::GameInputRouter( - std::shared_ptr game_keyboard, - std::shared_ptr game_gamepad, - std::shared_ptr pause_input, - std::shared_ptr pause_gamepad_input, + std::shared_ptr game_keyboard, + std::shared_ptr game_gamepad, + std::shared_ptr pause_input, + std::shared_ptr pause_gamepad_input, std::function on_pause_toggle) : game_keyboard_(std::move(game_keyboard)), game_gamepad_(std::move(game_gamepad)), pause_input_(std::move(pause_input)), diff --git a/arenai_desktop/src/controller/game_input_router.h b/arenai_desktop/src/controller/game_input_router.h index 216d8f5d..0ab77126 100644 --- a/arenai_desktop/src/controller/game_input_router.h +++ b/arenai_desktop/src/controller/game_input_router.h @@ -21,10 +21,10 @@ namespace arenai::desktop { public controller::AbstractGamepadCallback { public: GameInputRouter( - std::shared_ptr game_keyboard, - std::shared_ptr game_gamepad, - std::shared_ptr pause_input, - std::shared_ptr pause_gamepad_input, + std::shared_ptr game_keyboard, + std::shared_ptr game_gamepad, + std::shared_ptr pause_input, + std::shared_ptr pause_gamepad_input, std::function on_pause_toggle); void set_paused(bool paused); @@ -43,13 +43,13 @@ namespace arenai::desktop { void on_trigger(double z, controller::GamepadTrigger trigger) override; private: - const std::shared_ptr &keyboard_sink() const; - const std::shared_ptr &gamepad_sink() const; + const std::shared_ptr &keyboard_sink() const; + const std::shared_ptr &gamepad_sink() const; - std::shared_ptr game_keyboard_; - std::shared_ptr game_gamepad_; - std::shared_ptr pause_input_; - std::shared_ptr pause_gamepad_input_; + std::shared_ptr game_keyboard_; + std::shared_ptr game_gamepad_; + std::shared_ptr pause_input_; + std::shared_ptr pause_gamepad_input_; std::function on_pause_toggle_; bool paused_ = false; diff --git a/arenai_desktop/src/controller/gamepad.cpp b/arenai_desktop/src/controller/gamepad.cpp index b86d91f3..c8d898ef 100644 --- a/arenai_desktop/src/controller/gamepad.cpp +++ b/arenai_desktop/src/controller/gamepad.cpp @@ -8,19 +8,25 @@ namespace arenai::desktop { - namespace { - float apply_deadzone(const double value) { - constexpr double DEADZONE = 0.05; + float PlayerGamepadHandler::apply_dead_zone(const double value) { + constexpr double DEAD_ZONE = 0.05; - if (std::abs(value) < DEADZONE) return 0.f; + if (std::abs(value) < DEAD_ZONE) return 0.f; - // ramp from 0 at the deadzone edge up to ±1 at full deflection - const double sign = value > 0. ? 1. : -1.; - return static_cast(sign * (std::abs(value) - DEADZONE) / (1. - DEADZONE)); - } - }// namespace + // ramp from 0 at the deadzone edge up to ±1 at full deflection + const double sign = value > 0. ? 1. : -1.; + return static_cast(sign * (std::abs(value) - DEAD_ZONE) / (1. - DEAD_ZONE)); + } - PlayerGamepadHandler::PlayerGamepadHandler() : state{0., 0., 0., 0., 0., 0., std::nullopt} {} + PlayerGamepadHandler::PlayerGamepadHandler() + : state{ + .left_stick_x = 0., + .left_stick_y = 0., + .right_stick_x = 0., + .right_stick_y = 0., + .left_trigger = 0., + .right_trigger = 0., + .button = std::nullopt} {} void PlayerGamepadHandler::on_gamepad_button( const controller::GamepadButton button, const controller::InputAction action) { @@ -75,15 +81,19 @@ namespace arenai::desktop { // deflection is scaled into radians here (like the mouse handler) constexpr float factor = 0.02f * static_cast(M_PI); - turret_rotation = factor * apply_deadzone(event.right_stick_x); - canon_rotation = factor * apply_deadzone(event.right_stick_y); + turret_rotation = factor * apply_dead_zone(event.right_stick_x); + canon_rotation = factor * apply_dead_zone(event.right_stick_y); } - const float direction = apply_deadzone(event.left_stick_x); + const float direction = apply_dead_zone(event.left_stick_x); // triggers drive the tank: right forward, left backward (both in [0, 1]) const auto speed = static_cast(event.right_trigger - event.left_trigger); - return {true, {{direction, speed}, {turret_rotation, canon_rotation}, {need_fire}}}; + return { + true, + {.left_joystick = {.x = direction, .y = speed}, + .right_joystick = {.x = turret_rotation, .y = canon_rotation}, + .fire_button = {need_fire}}}; } }// namespace arenai::desktop diff --git a/arenai_desktop/src/controller/gamepad.h b/arenai_desktop/src/controller/gamepad.h index 5a5965d6..4f3ebd88 100644 --- a/arenai_desktop/src/controller/gamepad.h +++ b/arenai_desktop/src/controller/gamepad.h @@ -41,6 +41,8 @@ namespace arenai::desktop { private: PlayerGamepadInput state; + + static float apply_dead_zone(double value); }; }// namespace arenai::desktop diff --git a/arenai_desktop/src/controller/mouse_keyboard.cpp b/arenai_desktop/src/controller/mouse_keyboard.cpp index cecca95a..f3b0df95 100644 --- a/arenai_desktop/src/controller/mouse_keyboard.cpp +++ b/arenai_desktop/src/controller/mouse_keyboard.cpp @@ -4,7 +4,6 @@ #include "./mouse_keyboard.h" -#include #include namespace arenai::desktop { @@ -27,18 +26,26 @@ namespace arenai::desktop { void PlayerMouseKeyboardHandler::on_key( const controller::Key key, const controller::InputAction action) { - on_event({std::make_pair(key, action), std::nullopt, last_mouse_x, last_mouse_y}); + on_event( + {.key = std::make_pair(key, action), + .button = std::nullopt, + .mouse_x = last_mouse_x, + .mouse_y = last_mouse_y}); } void PlayerMouseKeyboardHandler::on_mouse_move(const double x, const double y) { last_mouse_x = x; last_mouse_y = y; - on_event({std::nullopt, std::nullopt, x, y}); + on_event({.key = std::nullopt, .button = std::nullopt, .mouse_x = x, .mouse_y = y}); } void PlayerMouseKeyboardHandler::on_mouse_button( const controller::MouseButton button, const controller::InputAction action) { - on_event({std::nullopt, std::make_pair(button, action), last_mouse_x, last_mouse_y}); + on_event( + {.key = std::nullopt, + .button = std::make_pair(button, action), + .mouse_x = last_mouse_x, + .mouse_y = last_mouse_y}); } std::tuple @@ -92,8 +99,8 @@ namespace arenai::desktop { // mouse buttons if (event.button) { - const auto [button, action] = *event.button; - if (button == controller::MouseButton::Left + if (const auto [button, action] = *event.button; + button == controller::MouseButton::Left && action == controller::InputAction::Press) { need_fire = true; cursor_captured = true; @@ -102,9 +109,9 @@ namespace arenai::desktop { return { true, - {{current_dir, current_speed}, - {current_turret_rotation, current_canon_rotation}, - {need_fire}}}; + {.left_joystick = {.x = current_dir, .y = current_speed}, + .right_joystick = {.x = current_turret_rotation, .y = current_canon_rotation}, + .fire_button = {need_fire}}}; } }// namespace arenai::desktop diff --git a/arenai_desktop/src/core/agent_loading_checker.cpp b/arenai_desktop/src/core/agent_loading_checker.cpp index 1c75338b..ca069915 100644 --- a/arenai_desktop/src/core/agent_loading_checker.cpp +++ b/arenai_desktop/src/core/agent_loading_checker.cpp @@ -29,7 +29,7 @@ namespace arenai::desktop { return "Missing file: " + e.missing_file().filename().string(); } catch (utils::ModelLoadException &e) { return "Error while loading file: " + e.wrong_state_dict_file().filename().string(); - } catch (const std::exception &e) { return "Unknow error while loading Model"; } + } catch (const std::exception &_) { return "Unknow error while loading Model"; } } }// namespace arenai::desktop diff --git a/arenai_desktop/src/core/game_environment.cpp b/arenai_desktop/src/core/game_environment.cpp index 2e92ad38..f7b575d5 100644 --- a/arenai_desktop/src/core/game_environment.cpp +++ b/arenai_desktop/src/core/game_environment.cpp @@ -24,7 +24,7 @@ namespace arenai::desktop { // The tank visions get their own headless backend (integrated GPU): their // synchronous readbacks are latency-bound on a discrete GPU, and this keeps // them off the window's GPU when the player view is offloaded (prime-run). - : core::BaseTanksEnvironment( + : BaseTanksEnvironment( std::make_shared(asset_folder_path), view::make_vulkan_backend(), nb_tanks, wanted_frequency, vision_height, vision_width, 8, true), @@ -119,7 +119,7 @@ namespace arenai::desktop { player_renderer->add_drawable( "cubemap", drawable_factory->make_cube_map(file_reader, "cubemap/1")); - std::uniform_real_distribution u_dist(0.f, 1.f); + std::uniform_real_distribution u_dist(0.f, 1.f); for (const auto &[name, shape]: player_tank->load_shell_shapes()) { const glm::vec4 color(u_dist(rng) * 0.8f, u_dist(rng) * 0.8f, u_dist(rng) * 0.8f, 1.f); diff --git a/arenai_desktop/src/game.cpp b/arenai_desktop/src/game.cpp index 0a63938f..d74452aa 100644 --- a/arenai_desktop/src/game.cpp +++ b/arenai_desktop/src/game.cpp @@ -100,17 +100,10 @@ namespace arenai::desktop { auto outcome = InGameOutcome::ExitGame; - // [ARENAI-DBG] temporary auto-repro - int dbg_auto_frames = -1; - if (const char *dbg = std::getenv("ARENAI_DEBUG_AUTOFRAMES")) - dbg_auto_frames = std::atoi(dbg); - int dbg_frame_count = 0; - const auto frame_dt = std::chrono::milliseconds(static_cast(game_options.wanted_frequency * 1000.f)); while (!window->should_close()) { - if (dbg_auto_frames > 0 && dbg_frame_count++ >= dbg_auto_frames) break; window->poll_events(); if (toggle_requested) { @@ -173,9 +166,8 @@ namespace arenai::desktop { void run_gui(const GameOptions &game_options, const ModelOptions &model_options) { // The view owns the window + GL context; the app only speaks the abstract // window/backend interface. - const std::shared_ptr graphics_backend = - view::make_glfw_vulkan_backend( - game_options.window_width, game_options.window_height, "ArenAI"); + const std::shared_ptr graphics_backend = view::make_glfw_vulkan_backend( + game_options.window_width, game_options.window_height, "ArenAI"); const auto window = graphics_backend->get_window(); std::cout << "Vulkan : " << graphics_backend->renderer_info() << std::endl; diff --git a/arenai_desktop/src/gui/rml/adapters.cpp b/arenai_desktop/src/gui/rml/adapters.cpp index 18302f16..09809ca1 100644 --- a/arenai_desktop/src/gui/rml/adapters.cpp +++ b/arenai_desktop/src/gui/rml/adapters.cpp @@ -21,7 +21,8 @@ namespace arenai::desktop::gui { Rml::FileHandle ReaderBackedFileInterface::Open(const Rml::String &path) { try { - auto file = std::make_unique(OpenedFile{reader_->read_text(path)}); + auto file = + std::make_unique(OpenedFile{.content = reader_->read_text(path)}); return reinterpret_cast(file.release()); } catch (const std::exception &e) { std::cerr << "RmlUi asset open failed: " << e.what() << std::endl; diff --git a/arenai_desktop/src/gui/rml/input.cpp b/arenai_desktop/src/gui/rml/input.cpp index 14a3d493..ec28eb4d 100644 --- a/arenai_desktop/src/gui/rml/input.cpp +++ b/arenai_desktop/src/gui/rml/input.cpp @@ -9,7 +9,8 @@ namespace arenai::desktop::gui { - std::vector ExplorerNavListener::visible_file_entries(Rml::Element *file_list) { + std::vector + ExplorerNavListener::visible_file_entries(const Rml::Element *file_list) { std::vector entries; for (int i = 0; i < file_list->GetNumChildren(); i++) if (Rml::Element *child = file_list->GetChild(i); child->IsVisible()) @@ -22,10 +23,10 @@ namespace arenai::desktop::gui { event.GetParameter("key_identifier", Rml::Input::KI_UNKNOWN)); if (key != Rml::Input::KI_UP && key != Rml::Input::KI_DOWN) return; - Rml::Element *focus = event.GetTargetElement(); + const Rml::Element *focus = event.GetTargetElement(); Rml::ElementDocument *document = focus->GetOwnerDocument(); if (document == nullptr) return; - Rml::Element *list = document->GetElementById("file-list"); + const Rml::Element *list = document->GetElementById("file-list"); Rml::Element *above = document->GetElementById("display-row"); Rml::Element *below = document->GetElementById("use-folder"); if (list == nullptr || above == nullptr || below == nullptr) return; diff --git a/arenai_desktop/src/gui/rml/input.h b/arenai_desktop/src/gui/rml/input.h index ffb14a2b..199fa532 100644 --- a/arenai_desktop/src/gui/rml/input.h +++ b/arenai_desktop/src/gui/rml/input.h @@ -30,7 +30,7 @@ namespace arenai::desktop::gui { public: // the data-for template stays in the list as a display:none // child; only its instantiated clones are real entries - static std::vector visible_file_entries(Rml::Element *file_list); + static std::vector visible_file_entries(const Rml::Element *file_list); void ProcessEvent(Rml::Event &event) override; }; diff --git a/arenai_desktop/src/gui/rml_menu.cpp b/arenai_desktop/src/gui/rml_menu.cpp index 3d4c89e1..c9fdf560 100644 --- a/arenai_desktop/src/gui/rml_menu.cpp +++ b/arenai_desktop/src/gui/rml_menu.cpp @@ -218,12 +218,18 @@ namespace arenai::desktop::gui { int weight; }; constexpr FontSpec MENU_FONTS[] = { - {"font/Sora-Regular.ttf", "Sora", 400}, - {"font/Sora-SemiBold.ttf", "Sora", 600}, - {"font/Sora-Bold.ttf", "Sora", 700}, - {"font/IBMPlexMono-Regular.ttf", "IBM Plex Mono", 400}, - {"font/IBMPlexMono-Medium.ttf", "IBM Plex Mono", 500}, - {"font/IBMPlexMono-SemiBold.ttf", "IBM Plex Mono", 600}, + {.path = "font/Sora-Regular.ttf", .family = "Sora", .weight = 400}, + {.path = "font/Sora-SemiBold.ttf", .family = "Sora", .weight = 600}, + {.path = "font/Sora-Bold.ttf", .family = "Sora", .weight = 700}, + {.path = "font/IBMPlexMono-Regular.ttf", + .family = "IBM Plex Mono", + .weight = 400}, + {.path = "font/IBMPlexMono-Medium.ttf", + .family = "IBM Plex Mono", + .weight = 500}, + {.path = "font/IBMPlexMono-SemiBold.ttf", + .family = "IBM Plex Mono", + .weight = 600}, }; font_buffers_.reserve(std::size(MENU_FONTS)); @@ -231,7 +237,7 @@ namespace arenai::desktop::gui { font_buffers_.push_back(asset_reader->read_text(path)); const auto &buffer = font_buffers_.back(); Rml::LoadFontFace( - Rml::Span( + Rml::Span( reinterpret_cast(buffer.data()), buffer.size()), family, Rml::Style::FontStyle::Normal, static_cast(weight)); @@ -393,7 +399,7 @@ namespace arenai::desktop::gui { } void focus_first_entry() const { - Rml::Element *list = params_document_->GetElementById("file-list"); + const Rml::Element *list = params_document_->GetElementById("file-list"); if (list == nullptr) return; const auto entries = ExplorerNavListener::visible_file_entries(list); if (entries.empty()) return; diff --git a/arenai_desktop/tests/include/arenai_desktop_tests/headless_windowed_backend.h b/arenai_desktop/tests/include/arenai_desktop_tests/headless_windowed_backend.h index 865c1355..bb7e1474 100644 --- a/arenai_desktop/tests/include/arenai_desktop_tests/headless_windowed_backend.h +++ b/arenai_desktop/tests/include/arenai_desktop_tests/headless_windowed_backend.h @@ -16,33 +16,32 @@ // tests discard. The agents' visions keep their real offscreen backend, built // internally by the environment. -class NoopDrawable final : public arenai::view::AbstractDrawable { +class NoopDrawable final : public view::AbstractDrawable { public: void draw( glm::mat4 mvp_matrix, glm::mat4 mv_matrix, glm::vec3 light_pos_from_camera, glm::vec3 camera_pos) override {} }; -class NoopDrawableFactory final : public arenai::view::AbstractDrawableFactory { +class NoopDrawableFactory final : public view::AbstractDrawableFactory { public: - std::unique_ptr make_cube_map( - const std::shared_ptr &file_reader, + std::unique_ptr make_cube_map( + const std::shared_ptr &file_reader, const std::filesystem::path &pngs_root_path) override { return std::make_unique(); } - std::unique_ptr make_diffuse( - const std::shared_ptr &file_reader, + std::unique_ptr make_diffuse( + const std::shared_ptr &file_reader, const std::vector> &vertices, glm::vec4 color) override { return std::make_unique(); } }; -class NoopPlayerRenderer final : public arenai::view::AbstractPlayerRenderer { +class NoopPlayerRenderer final : public view::AbstractPlayerRenderer { public: void add_drawable( - const std::string &name, - std::unique_ptr drawable) override {} + const std::string &name, std::unique_ptr drawable) override {} void remove_drawable(const std::string &name) override {} void draw(const std::vector> &model_matrices) override {} @@ -53,37 +52,34 @@ class NoopPlayerRenderer final : public arenai::view::AbstractPlayerRenderer { void make_current() const override {} void release_current() const override {} - void - add_hud_drawable(std::unique_ptr hud_drawable) override {} + void add_hud_drawable(std::unique_ptr hud_drawable) override {} void set_window_size(int new_width, int new_height) override {} }; -class HeadlessWindowedBackend final : public arenai::view::AbstractWindowedGraphicBackend { +class HeadlessWindowedBackend final : public view::AbstractWindowedGraphicBackend { public: - std::shared_ptr render_context() override { - return nullptr; - } + std::shared_ptr render_context() override { return nullptr; } - std::unique_ptr make_offscreen_renderer( + std::unique_ptr make_offscreen_renderer( int width, int height, glm::vec3 light_pos, - const std::shared_ptr &camera) override { + const std::shared_ptr &camera) override { return nullptr; } - std::shared_ptr drawable_factory() override { + std::shared_ptr drawable_factory() override { return std::make_shared(); } - std::shared_ptr hud_factory() override { return nullptr; } + std::shared_ptr hud_factory() override { return nullptr; } std::string renderer_info() override { return "headless test backend"; } void release_thread() override {} - std::shared_ptr get_window() override { return nullptr; } + std::shared_ptr get_window() override { return nullptr; } - std::unique_ptr make_player_renderer( - glm::vec3 light_pos, const std::shared_ptr &camera) override { + std::unique_ptr make_player_renderer( + glm::vec3 light_pos, const std::shared_ptr &camera) override { return std::make_unique(); } diff --git a/arenai_desktop/tests/include/arenai_desktop_tests/interceptor_agent.h b/arenai_desktop/tests/include/arenai_desktop_tests/interceptor_agent.h index 29754151..addbd5c6 100644 --- a/arenai_desktop/tests/include/arenai_desktop_tests/interceptor_agent.h +++ b/arenai_desktop/tests/include/arenai_desktop_tests/interceptor_agent.h @@ -12,20 +12,23 @@ // Records every batch of states the game loop hands to the agent and answers // with neutral actions: what act() received is exactly what the SAC agent // would have seen in run_game(). -class InterceptorAgent final : public arenai::agent::AbstractAgent { +class InterceptorAgent final : public agent::AbstractAgent { public: - std::vector> received_states; + std::vector> received_states; int last_vision_height = -1; int last_vision_width = -1; - std::vector - act(const std::vector &states, const int vision_height, + std::vector + act(const std::vector &states, const int vision_height, const int vision_width) override { received_states.push_back(states); last_vision_height = vision_height; last_vision_width = vision_width; - return std::vector(states.size(), {{0.f, 0.f}, {0.f, 0.f}, {false}}); + return std::vector( + states.size(), {.left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {false}}); } void load(const std::filesystem::path &agent_folder) override {} diff --git a/arenai_desktop/tests/src/tests_e2e_agent_input.cpp b/arenai_desktop/tests/src/tests_e2e_agent_input.cpp index 08c7e475..8a3dec7c 100644 --- a/arenai_desktop/tests/src/tests_e2e_agent_input.cpp +++ b/arenai_desktop/tests/src/tests_e2e_agent_input.cpp @@ -91,7 +91,7 @@ namespace { void write_golden(const std::filesystem::path &golden_path, const std::vector &pixels) { std::filesystem::create_directories(golden_path.parent_path()); - nlohmann::json output_json(pixels); + const nlohmann::json output_json(pixels); std::ofstream output_file(golden_path); output_file << output_json; } From 24f1708fa6b3e399fd99f73e536133814dc1f69e Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Fri, 14 Aug 2026 01:34:10 +0200 Subject: [PATCH 34/70] constant entropy coefficient for PPO + smaller spawn zone --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 87 ++----------------- arenai_agent/src/agents/ppo/ppo_trainer.h | 22 +---- arenai_agent/src/main.cpp | 8 +- .../tests/src/tests_agents/tests_ppo.cpp | 7 +- 4 files changed, 18 insertions(+), 106 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index ae4c387d..f8e645b8 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -44,34 +44,23 @@ namespace arenai::agent { critic(std::make_shared( vision_height, vision_width, nb_sensors, hidden_size_sensors, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous(std::make_shared(0.001f)), - alpha_discrete(std::make_shared(0.001f)), - continuous_target_entropy(std::make_shared( - nb_continuous_actions, TARGET_SIGMA)), - discrete_target_entropy( - std::make_shared(TARGET_FIRE_PROBABILITY)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_optim(std::make_unique( critic->parameters(), torch::optim::AdamOptions(critic_learning_rate))), - alpha_continuous_optim(std::make_unique( - alpha_continuous->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), - alpha_discrete_optim(std::make_unique( - alpha_discrete->parameters(), torch::optim::AdamOptions(alpha_learning_rate))), actor_mean_loss_metric(std::make_shared("π_μ", metric_window_size)), actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), critic_std_loss_metric(std::make_shared("v_σ", metric_window_size)), continuous_entropy_metric(std::make_shared("Hc", metric_window_size)), discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), - alpha_continuous_metric(std::make_shared("α_c", metric_window_size, 2, true)), - alpha_discrete_metric(std::make_shared("α_d", metric_window_size, 2, true)), sigma_metric(std::make_shared("σ", metric_window_size)), clip_fraction_metric(std::make_shared("clip", metric_window_size)), kl_metric(std::make_shared("kl", metric_window_size, 2, true)), gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), grad_norm_max(grad_norm_max), epochs(epochs), rollout_size(rollout_size), - minibatch_size(minibatch_size) { + minibatch_size(minibatch_size), continuous_entropy_coefficient(0.001f), + discrete_entropy_coefficient(0.001f) { to(device); } @@ -147,19 +136,6 @@ namespace arenai::agent { train_critic(mb_vision, mb_proprioception, select(flat_returns, idx)); } } - - // dual ascent, once per rollout on the mean entropy of the whole update: outside the - // epoch loop, an early KL stop can no longer freeze the entropy regulation - if (nb_actor_minibatches > 0) { - const auto nb_minibatches = static_cast(nb_actor_minibatches); - - train_alpha( - alpha_continuous, alpha_continuous_optim, continuous_target_entropy, - continuous_entropy_sum / nb_minibatches, alpha_continuous_metric); - train_alpha( - alpha_discrete, alpha_discrete_optim, discrete_target_entropy, - discrete_entropy_sum / nb_minibatches, alpha_discrete_metric); - } } ActorUpdateResult PpoTrainer::train_actor( @@ -205,8 +181,8 @@ namespace arenai::agent { const auto surrogate = torch::min(ratio * advantages, clipped_ratio * advantages); const auto actor_loss = -torch::mean( - surrogate + alpha_continuous->alpha().detach() * continuous_entropy - + alpha_discrete->alpha().detach() * discrete_entropy); + surrogate + continuous_entropy_coefficient * continuous_entropy + + discrete_entropy_coefficient * discrete_entropy); actor_optim->zero_grad(); actor_loss.backward(); @@ -243,24 +219,6 @@ namespace arenai::agent { critic_std_loss_metric->add(loss_value); } - void PpoTrainer::train_alpha( - const std::shared_ptr &alpha, - const std::shared_ptr &optim, - const std::shared_ptr &target_entropy, const float mean_entropy, - const std::shared_ptr &alpha_metric) { - - // entropy above target -> alpha decays toward 0 (stops pushing sigma up); - // entropy below target -> alpha grows (prevents entropy collapse) - const auto alpha_loss = - torch::mean(alpha->log_alpha() * (mean_entropy - target_entropy->target_entropy())); - - optim->zero_grad(); - alpha_loss.backward(); - optim->step(); - - alpha_metric->add(alpha->alpha().item()); - } - GaeResult PpoTrainer::compute_gae(const PpoRollout &rollout, const torch::Device device) const { torch::NoGradGuard no_grad; @@ -318,18 +276,9 @@ namespace arenai::agent { } std::vector> PpoTrainer::get_metrics() { - return { - actor_mean_loss_metric, - actor_std_loss_metric, - critic_mean_loss_metric, - critic_std_loss_metric, - continuous_entropy_metric, - discrete_entropy_metric, - alpha_continuous_metric, - alpha_discrete_metric, - sigma_metric, - clip_fraction_metric, - kl_metric}; + return {actor_mean_loss_metric, actor_std_loss_metric, critic_mean_loss_metric, + critic_std_loss_metric, continuous_entropy_metric, discrete_entropy_metric, + sigma_metric, clip_fraction_metric, kl_metric}; } void PpoTrainer::save(const std::filesystem::path &output_folder) { @@ -337,16 +286,10 @@ namespace arenai::agent { save_torch(output_folder, actor, "actor.pt"); save_torch(output_folder, critic, "critic.pt"); - save_torch(output_folder, alpha_continuous, "alpha_continuous.pt"); - save_torch(output_folder, alpha_discrete, "alpha_discrete.pt"); - // Optimizers save_torch(output_folder, actor_optim, "actor_optim.pt"); save_torch(output_folder, critic_optim, "critic_optim.pt"); - save_torch(output_folder, alpha_continuous_optim, "alpha_continuous_optim.pt"); - save_torch(output_folder, alpha_discrete_optim, "alpha_discrete_optim.pt"); - // string repr std::ostringstream actor_repr_oss; dump_module_tree(actor, actor_repr_oss, 0, "actor"); @@ -364,30 +307,16 @@ namespace arenai::agent { void PpoTrainer::set_train(const bool train) const { actor->train(train); critic->train(train); - - alpha_continuous->train(train); - alpha_discrete->train(train); - - continuous_target_entropy->train(train); - discrete_target_entropy->train(train); } void PpoTrainer::to(const torch::Device device) const { actor->to(device); critic->to(device); - - alpha_continuous->to(device); - alpha_discrete->to(device); - - continuous_target_entropy->to(device); - discrete_target_entropy->to(device); } int PpoTrainer::count_parameters() { return count_parameters_impl(actor->parameters()) - + count_parameters_impl(critic->parameters()) - + count_parameters_impl(alpha_continuous->parameters()) - + count_parameters_impl(alpha_discrete->parameters()); + + count_parameters_impl(critic->parameters()); } }// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index f2c1863a..51c3f965 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -51,16 +51,8 @@ namespace arenai::agent { std::shared_ptr critic; - // adaptive entropy coefficients (dual ascent toward fixed entropy targets) - std::shared_ptr alpha_continuous; - std::shared_ptr alpha_discrete; - std::shared_ptr continuous_target_entropy; - std::shared_ptr discrete_target_entropy; - std::shared_ptr actor_optim; std::shared_ptr critic_optim; - std::shared_ptr alpha_continuous_optim; - std::shared_ptr alpha_discrete_optim; std::shared_ptr actor_mean_loss_metric; std::shared_ptr actor_std_loss_metric; @@ -71,9 +63,6 @@ namespace arenai::agent { std::shared_ptr continuous_entropy_metric; std::shared_ptr discrete_entropy_metric; - std::shared_ptr alpha_continuous_metric; - std::shared_ptr alpha_discrete_metric; - // mean sigma of the truncated normal: direct view of the aim spread, Hc only bounds it std::shared_ptr sigma_metric; @@ -94,6 +83,9 @@ namespace arenai::agent { // rows per gradient step; also the chunk size for the no-grad value evaluation int minibatch_size; + float continuous_entropy_coefficient; + float discrete_entropy_coefficient; + void train() const; // one backward pass on the actor for a single minibatch; the update is skipped @@ -109,14 +101,6 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &returns) const; - // one backward pass of the dual ascent on a single entropy coefficient, fed with - // the mean entropy of the whole update - static void train_alpha( - const std::shared_ptr &alpha, - const std::shared_ptr &optim, - const std::shared_ptr &target_entropy, float mean_entropy, - const std::shared_ptr &alpha_metric); - // GAE advantages (normalized over the valid pairs) and value targets, // computed with the pre-update critic GaeResult compute_gae(const PpoRollout &rollout, torch::Device device) const; diff --git a/arenai_agent/src/main.cpp b/arenai_agent/src/main.cpp index 9ae9db50..22f358c9 100644 --- a/arenai_agent/src/main.cpp +++ b/arenai_agent/src/main.cpp @@ -34,10 +34,10 @@ int main(const int argc, char **argv) { parser.add_argument("--nb_tanks").scan<'i', int>().default_value(32); parser.add_argument("--vision_height").scan<'i', int>().default_value(128); parser.add_argument("--vision_width").scan<'i', int>().default_value(256); - parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(750.f); - parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(750.f); - parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(750.f); - parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(750.f); + parser.add_argument("--initial_spawn_width").scan<'g', float>().default_value(500.f); + parser.add_argument("--initial_spawn_height").scan<'g', float>().default_value(500.f); + parser.add_argument("--final_spawn_width").scan<'g', float>().default_value(500.f); + parser.add_argument("--final_spawn_height").scan<'g', float>().default_value(500.f); parser.add_argument("--vision_num_threads") .scan<'i', int>() .default_value(static_cast(std::thread::hardware_concurrency())); diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index 33195fee..0f07da2e 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -76,7 +76,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 11); + ASSERT_EQ(metrics.size(), 9); } // ======================================================================== @@ -145,9 +145,8 @@ TEST_P(PpoSaveLoadParamTest, SaveCreatesExpectedFiles) { factory->get_trainer()->save(save_dir); const std::vector expected_files = { - "actor.pt", "critic.pt", "alpha_continuous.pt", "alpha_discrete.pt", - "actor_optim.pt", "critic_optim.pt", "alpha_continuous_optim.pt", "alpha_discrete_optim.pt", - "actor_repr.txt", "critic_repr.txt", + "actor.pt", "critic.pt", "actor_optim.pt", + "critic_optim.pt", "actor_repr.txt", "critic_repr.txt", }; for (const auto &f: expected_files) From 6d9f818d63ad0309795ff47ae0af452f0c620e3d Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Fri, 14 Aug 2026 11:53:37 +0200 Subject: [PATCH 35/70] * shell reserve regeneration (1 shell each 1.5s) * target sigma from 0.2 to 0.1 --- arenai_agent/src/networks/constants.h | 2 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 23 +++++-- arenai_model/src/tank/jolt_enemy_tank.h | 2 + .../src/tests_enemy_tank/tests_enemy_tank.cpp | 61 +++++++++++++++++++ 4 files changed, 82 insertions(+), 6 deletions(-) diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index 99b0bc3f..b70322f5 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -8,7 +8,7 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; - constexpr float TARGET_SIGMA = 0.2f; + constexpr float TARGET_SIGMA = 0.1f; constexpr float TARGET_FIRE_PROBABILITY = 0.05f; constexpr float SIGMA_MIN = 1e-4f; diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 65bee910..3d752345 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -60,7 +60,13 @@ namespace arenai::model { // caused it — a scarcity the policy can see (proprioception) rather than a // per-shot penalty, which would make firing negative before aiming is learned hit_received_cost(0.1f), initial_nb_shells(10), nb_shells(initial_nb_shells), - shells_recharged_per_hit(5), is_dead_already_triggered(false), has_touch(false), + shells_recharged_per_hit(5), + // the reserve also refills on its own, one shell every 1.5 s: with the recharge + // conditioned on a hit alone, a policy that never hits stays dry for the rest of + // its life — an absorbing state where the fire action is a no-op and stops + // producing any gradient. The hit recharge remains the fast path + nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), + curr_frame_shell_regen(0), is_dead_already_triggered(false), has_touch(false), has_fired(false) {} float JoltEnemyTank::compute_hit_reward( @@ -138,10 +144,17 @@ namespace arenai::model { curr_frame_upside_down++; else curr_frame_upside_down = 0; - // 2. dead / suicide penalty + // 2. passive shell regeneration, capped at the initial reserve: the hit recharge + // may push the reserve above it, regeneration never does + if (++curr_frame_shell_regen >= nb_frames_per_shell_regen) { + curr_frame_shell_regen = 0; + if (nb_shells < initial_nb_shells) nb_shells++; + } + + // 3. dead / suicide penalty const auto dead_penalty = is_dead() ? -1.f : 0.f; - // 3. fired shells: sample the closest tank along the trajectory and pay the + // 4. fired shells: sample the closest tank along the trajectory and pay the // dispersion gaussian (plus hit/kill bonuses) once the shell dies; firing itself // is free — the limited shell reserve (recharged on hit) taxes the spam float shells_reward = 0.f; @@ -170,11 +183,11 @@ namespace arenai::model { tracked_shells.erase(tracked_shells.begin() + i); } - // 4. hits received penalty + // 5. hits received penalty const float hit_received_penalty = -hit_received_cost * static_cast(get_received_hits()); - // 5. total reward + // 6. total reward const float reward = dead_penalty + shells_reward + hit_received_penalty; return reward; diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index 3a63aa4d..7674f0bf 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -74,6 +74,8 @@ namespace arenai::model { int initial_nb_shells; int nb_shells; int shells_recharged_per_hit; + int nb_frames_per_shell_regen; + int curr_frame_shell_regen; bool is_dead_already_triggered; diff --git a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp index d2998a4d..4ac38bbb 100644 --- a/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp +++ b/arenai_model/tests/src/tests_enemy_tank/tests_enemy_tank.cpp @@ -232,3 +232,64 @@ TEST_F(EnemyTankTest, HasHitOtherTankResetsAfterCall) { ASSERT_FALSE(shared_a->has_hit_other_tank()) << "has_hit_other_tank should reset to false after being queried"; } + +// ======================================================================== +// Shell reserve — passive regeneration +// ======================================================================== + +TEST_F(EnemyTankTest, ShellReserveRegeneratesOverTime) { + // the fixture engine runs at 60 Hz: one reward call per frame, one shell every 1.5 s. + // the bounds stay a frame away from the period so the test does not depend on how the + // period rounds to an integer number of frames + constexpr int nb_frames_one_second = 60; + + add_ground(); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + + for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); + + const std::shared_ptr shared_tank(tank.release()); + const std::vector tanks{shared_tank}; + + ASSERT_FLOAT_EQ(shared_tank->get_proprioception().back(), 1.f) + << "the reserve should start full"; + + // fire one shell: the reserve drops by one + constexpr user_input fire_input{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; + for (const auto &ctrl: shared_tank->get_controllers()) ctrl->apply_input(fire_input); + engine->step(1.f / 60.f); + + const float reserve_after_fire = shared_tank->get_proprioception().back(); + ASSERT_LT(reserve_after_fire, 1.f) << "firing should consume a shell"; + + // after 1 s the period is not over yet: still nothing + for (int i = 0; i < nb_frames_one_second; i++) shared_tank->get_reward(tanks); + ASSERT_FLOAT_EQ(shared_tank->get_proprioception().back(), reserve_after_fire) + << "the reserve should not regenerate before the full period has elapsed"; + + // after 2 s the shell is back + for (int i = 0; i < nb_frames_one_second; i++) shared_tank->get_reward(tanks); + ASSERT_FLOAT_EQ(shared_tank->get_proprioception().back(), 1.f) + << "one shell should be given back after 1.5 s"; +} + +TEST_F(EnemyTankTest, ShellReserveRegenerationIsCappedAtInitialReserve) { + constexpr int nb_frames_one_second = 60; + + add_ground(); + auto tank = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + + for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); + + const std::shared_ptr shared_tank(tank.release()); + const std::vector tanks{shared_tank}; + + // the reserve is already full: several periods must not push it above it + for (int i = 0; i < 5 * nb_frames_one_second; i++) shared_tank->get_reward(tanks); + + ASSERT_FLOAT_EQ(shared_tank->get_proprioception().back(), 1.f) + << "regeneration should never take the reserve above its initial value"; +} From f4fd34ab14b4d8925b2086151d9faff19a780cd0 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 16 Aug 2026 19:30:38 +0200 Subject: [PATCH 36/70] one alpha per continuous action (PPO under training, do the same for SAC) --- arenai_agent/src/agents/ppo/ppo_factory.cpp | 3 +- .../src/agents/ppo/ppo_hyperparams.cpp | 3 +- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 14 +- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 142 ++++++++++++++++-- arenai_agent/src/agents/ppo/ppo_trainer.h | 39 ++++- arenai_agent/src/agents/sac/sac_trainer.cpp | 36 +++-- arenai_agent/src/agents/sac/sac_trainer.h | 4 +- arenai_agent/src/networks/constants.h | 4 +- arenai_agent/src/networks/entropy.cpp | 23 +++ arenai_agent/src/networks/entropy.h | 31 ++++ .../tests/src/tests_agents/tests_ppo.cpp | 10 +- .../src/tests_agents/tests_ppo_training.cpp | 5 +- 12 files changed, 266 insertions(+), 48 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_factory.cpp b/arenai_agent/src/agents/ppo/ppo_factory.cpp index 8ab554af..74be5d18 100644 --- a/arenai_agent/src/agents/ppo/ppo_factory.cpp +++ b/arenai_agent/src/agents/ppo/ppo_factory.cpp @@ -22,7 +22,8 @@ namespace arenai::agent { agent(std::make_shared(actor, device, collector)), trainer(std::make_shared( actor, rollout_buffer, vision_height, vision_width, nb_sensors, nb_continuous_actions, - params.actor_learning_rate, params.critic_learning_rate, params.alpha_learning_rate, + params.actor_learning_rate, params.critic_learning_rate, + params.continuous_alpha_learning_rate, params.discrete_alpha_learning_rate, params.hidden_size_sensors, params.critic_hidden_sizes, params.vision_channels, params.group_norm_nums, device, params.metric_window_size, params.gamma, params.gae_lambda, params.clip_epsilon, params.target_kl, params.grad_norm_max, diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp index f8d9d6a1..713f1817 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp @@ -21,7 +21,8 @@ namespace arenai::agent { {"--clip_epsilon", &PpoHyperParams::clip_epsilon}, {"--target_kl", &PpoHyperParams::target_kl}, {"--grad_norm_max", &PpoHyperParams::grad_norm_max}, - {"--alpha_learning_rate", &PpoHyperParams::alpha_learning_rate}, + {"--continuous_alpha_learning_rate", &PpoHyperParams::continuous_alpha_learning_rate}, + {"--discrete_alpha_learning_rate", &PpoHyperParams::discrete_alpha_learning_rate}, {"--epochs", &PpoHyperParams::epochs}, {"--rollout_size", &PpoHyperParams::rollout_size}, {"--minibatch_size", &PpoHyperParams::minibatch_size}, diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index d3dc95c6..7f20d624 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -32,8 +32,18 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - // lr of the SAC-style adaptive entropy alphas (fixed targets: sigma 0.1, fire proba 0.2) - float alpha_learning_rate = 1e-2f; + // lr of the SAC-style adaptive entropy alphas (fixed targets: sigma 0.2, fire proba 0.15). + // Plain SGD, so the step is lr * (entropy - target): proportional to the error, it slows + // down near the target instead of the fixed +-lr of a normalized optimizer, which drove + // the alpha into a limit cycle. Two rates because the two errors live on different + // scales: the continuous entropy is summed over the actions (error ~1 nat), the discrete + // one is a single binary entropy (error ~0.15 nat). + // Sized for reaction speed alone, not for windup: the MultiAlphaParameters bounds cap + // the drift whatever the rate. At these values an alpha crosses its whole admissible + // range in ~130 rollouts once the entropy leaves its target, against ~1300 an order of + // magnitude lower - too slow to catch an entropy collapse. + float continuous_alpha_learning_rate = 1e-2f; + float discrete_alpha_learning_rate = 1e-1f; int epochs = 4; int rollout_size = 30 * 30; int minibatch_size = 1024; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index f8e645b8..606b10fd 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -8,6 +8,7 @@ #include "../../distributions/multinomial.h" #include "../../distributions/truncated_normal.h" +#include "../../metrics/last_metric.h" #include "../../metrics/mean_metric.h" #include "../../metrics/std_metric.h" #include "../../networks/constants.h" @@ -33,8 +34,9 @@ namespace arenai::agent { std::shared_ptr actor, std::shared_ptr rollout_buffer, const int vision_height, const int vision_width, const int nb_sensors, const int nb_continuous_actions, const float actor_learning_rate, - const float critic_learning_rate, const float alpha_learning_rate, - const int hidden_size_sensors, const std::vector &critic_hidden_sizes, + const float critic_learning_rate, const float continuous_alpha_learning_rate, + const float discrete_alpha_learning_rate, const int hidden_size_sensors, + const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, const torch::Device device, const int metric_window_size, const float gamma, const float gae_lambda, @@ -44,23 +46,44 @@ namespace arenai::agent { critic(std::make_shared( vision_height, vision_width, nb_sensors, hidden_size_sensors, critic_hidden_sizes, vision_channels, group_norm_nums)), + // the bounds are anti-windup guards, two decades on each side of the initial value: + // they stay inactive as long as the dual ascent behaves + alpha_continuous( + std::make_shared(1e-3f, 1e-5f, 1e-2f, nb_continuous_actions)), + alpha_discrete(std::make_shared(1e-3f, 1e-5f, 1e-2f, 1)), + continuous_target_entropy( + std::make_shared(TARGET_SIGMA)), + discrete_target_entropy( + std::make_shared(TARGET_FIRE_PROBABILITY)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_optim(std::make_unique( critic->parameters(), torch::optim::AdamOptions(critic_learning_rate))), + alpha_continuous_optim(std::make_unique( + alpha_continuous->parameters(), + torch::optim::SGDOptions(continuous_alpha_learning_rate))), + alpha_discrete_optim(std::make_unique( + alpha_discrete->parameters(), + torch::optim::SGDOptions(discrete_alpha_learning_rate))), actor_mean_loss_metric(std::make_shared("π_μ", metric_window_size)), actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), critic_std_loss_metric(std::make_shared("v_σ", metric_window_size)), continuous_entropy_metric(std::make_shared("Hc", metric_window_size)), discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), + alpha_continuous_metric(std::make_shared("α_c", metric_window_size, 2, true)), + alpha_continuous_max_metric( + std::make_shared("α_c_max", metric_window_size, 2, true)), + alpha_discrete_metric(std::make_shared("α_d", metric_window_size, 2, true)), + continuous_target_entropy_metric(std::make_shared("Hc_t")), + discrete_target_entropy_metric(std::make_shared("Hd_t")), sigma_metric(std::make_shared("σ", metric_window_size)), + sigma_min_metric(std::make_shared("σ_min", metric_window_size)), clip_fraction_metric(std::make_shared("clip", metric_window_size)), kl_metric(std::make_shared("kl", metric_window_size, 2, true)), gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), grad_norm_max(grad_norm_max), epochs(epochs), rollout_size(rollout_size), - minibatch_size(minibatch_size), continuous_entropy_coefficient(0.001f), - discrete_entropy_coefficient(0.001f) { + minibatch_size(minibatch_size) { to(device); } @@ -100,7 +123,9 @@ namespace arenai::agent { return tensor.index_select(0, idx).to(device); }; - float continuous_entropy_sum = 0.f; + // one running sum per continuous action; left undefined until the first minibatch so + // the action count does not have to be carried around + torch::Tensor continuous_entropy_sum; float discrete_entropy_sum = 0.f; int nb_actor_minibatches = 0; @@ -124,7 +149,9 @@ namespace arenai::agent { select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), select(flat_advantages, idx)); - continuous_entropy_sum += continuous_entropy; + continuous_entropy_sum = continuous_entropy_sum.defined() + ? continuous_entropy_sum + continuous_entropy + : continuous_entropy; discrete_entropy_sum += discrete_entropy; nb_actor_minibatches++; @@ -136,6 +163,31 @@ namespace arenai::agent { train_critic(mb_vision, mb_proprioception, select(flat_returns, idx)); } } + + // dual ascent, once per rollout on the mean entropy of the whole update: outside the + // epoch loop, an early KL stop can no longer freeze the entropy regulation + if (nb_actor_minibatches > 0) { + const auto nb_minibatches = static_cast(nb_actor_minibatches); + + train_alpha( + alpha_continuous, alpha_continuous_optim, continuous_target_entropy, + continuous_entropy_sum / nb_minibatches); + train_alpha( + alpha_discrete, alpha_discrete_optim, discrete_target_entropy, + torch::tensor( + discrete_entropy_sum / nb_minibatches, + torch::TensorOptions().device(continuous_entropy_sum.device()))); + + const auto continuous_alphas = alpha_continuous->alpha(); + alpha_continuous_metric->add(continuous_alphas.mean().item()); + alpha_continuous_max_metric->add(continuous_alphas.max().item()); + alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); + } + + continuous_target_entropy_metric->add( + continuous_target_entropy->target_entropy().item()); + discrete_target_entropy_metric->add( + discrete_target_entropy->target_entropy().item()); } ActorUpdateResult PpoTrainer::train_actor( @@ -156,15 +208,18 @@ namespace arenai::agent { const auto ratio = torch::exp(curr_continuous_log_probs + curr_discrete_log_probs - old_log_probs); - const auto continuous_entropy = truncated_normal_entropy(mu, sigma).sum(-1, true); + // kept per action: each dimension is regulated by its own coefficient, summing here + // would let a wide dimension hide a collapsed one + const auto continuous_entropy = truncated_normal_entropy(mu, sigma); const auto discrete_entropy = multinomial_entropy(discrete_proba); - const auto mean_continuous_entropy = continuous_entropy.mean().item(); + const auto mean_continuous_entropy = continuous_entropy.mean(0).detach(); const auto mean_discrete_entropy = discrete_entropy.mean().item(); - continuous_entropy_metric->add(mean_continuous_entropy); + continuous_entropy_metric->add(mean_continuous_entropy.mean().item()); discrete_entropy_metric->add(mean_discrete_entropy); sigma_metric->add(sigma.mean().item()); + sigma_min_metric->add(sigma.mean(0).min().item()); // approx KL (Schulman): E[(ratio - 1) - log ratio]; skip this minibatch when the // policy drifted too far from the rollout policy @@ -180,9 +235,13 @@ namespace arenai::agent { const auto surrogate = torch::min(ratio * advantages, clipped_ratio * advantages); + // one coefficient per action dimension, broadcast over the batch and summed back + const auto continuous_entropy_bonus = + (alpha_continuous->alpha().detach() * continuous_entropy).sum(-1, true); + const auto actor_loss = -torch::mean( - surrogate + continuous_entropy_coefficient * continuous_entropy - + discrete_entropy_coefficient * discrete_entropy); + surrogate + continuous_entropy_bonus + + alpha_discrete->alpha().detach() * discrete_entropy); actor_optim->zero_grad(); actor_loss.backward(); @@ -219,6 +278,26 @@ namespace arenai::agent { critic_std_loss_metric->add(loss_value); } + void PpoTrainer::train_alpha( + const std::shared_ptr &alpha, + const std::shared_ptr &optim, + const std::shared_ptr &target_entropy, + const torch::Tensor &mean_entropy) { + + // entropy above target -> alpha decays toward 0 (stops pushing sigma up); + // entropy below target -> alpha grows (prevents entropy collapse). + // SGD, not Adam: the step must stay proportional to the entropy error, a normalized + // step keeps its full speed next to the target and makes the loop oscillate. + // Summed and not averaged: each coefficient must receive its own error untouched, + // averaging would divide every gradient by the number of dimensions + const auto alpha_loss = + torch::sum(alpha->log_alpha() * (mean_entropy - target_entropy->target_entropy())); + + optim->zero_grad(); + alpha_loss.backward(); + optim->step(); + } + GaeResult PpoTrainer::compute_gae(const PpoRollout &rollout, const torch::Device device) const { torch::NoGradGuard no_grad; @@ -276,9 +355,22 @@ namespace arenai::agent { } std::vector> PpoTrainer::get_metrics() { - return {actor_mean_loss_metric, actor_std_loss_metric, critic_mean_loss_metric, - critic_std_loss_metric, continuous_entropy_metric, discrete_entropy_metric, - sigma_metric, clip_fraction_metric, kl_metric}; + return { + actor_mean_loss_metric, + actor_std_loss_metric, + critic_mean_loss_metric, + critic_std_loss_metric, + continuous_target_entropy_metric, + continuous_entropy_metric, + alpha_continuous_metric, + alpha_continuous_max_metric, + discrete_target_entropy_metric, + discrete_entropy_metric, + alpha_discrete_metric, + sigma_metric, + sigma_min_metric, + clip_fraction_metric, + kl_metric}; } void PpoTrainer::save(const std::filesystem::path &output_folder) { @@ -286,10 +378,16 @@ namespace arenai::agent { save_torch(output_folder, actor, "actor.pt"); save_torch(output_folder, critic, "critic.pt"); + save_torch(output_folder, alpha_continuous, "alpha_continuous.pt"); + save_torch(output_folder, alpha_discrete, "alpha_discrete.pt"); + // Optimizers save_torch(output_folder, actor_optim, "actor_optim.pt"); save_torch(output_folder, critic_optim, "critic_optim.pt"); + save_torch(output_folder, alpha_continuous_optim, "alpha_continuous_optim.pt"); + save_torch(output_folder, alpha_discrete_optim, "alpha_discrete_optim.pt"); + // string repr std::ostringstream actor_repr_oss; dump_module_tree(actor, actor_repr_oss, 0, "actor"); @@ -307,16 +405,30 @@ namespace arenai::agent { void PpoTrainer::set_train(const bool train) const { actor->train(train); critic->train(train); + + alpha_continuous->train(train); + alpha_discrete->train(train); + + continuous_target_entropy->train(train); + discrete_target_entropy->train(train); } void PpoTrainer::to(const torch::Device device) const { actor->to(device); critic->to(device); + + alpha_continuous->to(device); + alpha_discrete->to(device); + + continuous_target_entropy->to(device); + discrete_target_entropy->to(device); } int PpoTrainer::count_parameters() { return count_parameters_impl(actor->parameters()) - + count_parameters_impl(critic->parameters()); + + count_parameters_impl(critic->parameters()) + + count_parameters_impl(alpha_continuous->parameters()) + + count_parameters_impl(alpha_discrete->parameters()); } }// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 51c3f965..7019f8f0 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -21,7 +21,8 @@ namespace arenai::agent { struct ActorUpdateResult { // the minibatch drifted past the KL threshold: no update was applied bool kl_exceeded; - float continuous_entropy; + // one entropy per continuous action, detached: each dimension feeds its own alpha + torch::Tensor continuous_entropy; float discrete_entropy; }; @@ -30,7 +31,8 @@ namespace arenai::agent { PpoTrainer( std::shared_ptr actor, std::shared_ptr rollout_buffer, int vision_height, int vision_width, int nb_sensors, int nb_continuous_actions, - float actor_learning_rate, float critic_learning_rate, float alpha_learning_rate, + float actor_learning_rate, float critic_learning_rate, + float continuous_alpha_learning_rate, float discrete_alpha_learning_rate, int hidden_size_sensors, const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, torch::Device device, int metric_window_size, @@ -51,8 +53,16 @@ namespace arenai::agent { std::shared_ptr critic; + // adaptive entropy coefficients (dual ascent toward fixed entropy targets) + std::shared_ptr alpha_continuous; + std::shared_ptr alpha_discrete; + std::shared_ptr continuous_target_entropy; + std::shared_ptr discrete_target_entropy; + std::shared_ptr actor_optim; std::shared_ptr critic_optim; + std::shared_ptr alpha_continuous_optim; + std::shared_ptr alpha_discrete_optim; std::shared_ptr actor_mean_loss_metric; std::shared_ptr actor_std_loss_metric; @@ -63,9 +73,23 @@ namespace arenai::agent { std::shared_ptr continuous_entropy_metric; std::shared_ptr discrete_entropy_metric; + std::shared_ptr alpha_continuous_metric; + // alpha of the most constrained dimension: it is the one that rises when a single + // action collapses, which the mean hides + std::shared_ptr alpha_continuous_max_metric; + std::shared_ptr alpha_discrete_metric; + + std::shared_ptr continuous_target_entropy_metric; + std::shared_ptr discrete_target_entropy_metric; + // mean sigma of the truncated normal: direct view of the aim spread, Hc only bounds it std::shared_ptr sigma_metric; + // sigma of the tightest action dimension: the entropy target constrains the sum over + // the dimensions, so a single wide one can hide several collapsed ones behind a healthy + // Hc — and a collapsed dimension is what blows the log ratio up + std::shared_ptr sigma_min_metric; + std::shared_ptr clip_fraction_metric; std::shared_ptr kl_metric; @@ -83,9 +107,6 @@ namespace arenai::agent { // rows per gradient step; also the chunk size for the no-grad value evaluation int minibatch_size; - float continuous_entropy_coefficient; - float discrete_entropy_coefficient; - void train() const; // one backward pass on the actor for a single minibatch; the update is skipped @@ -101,6 +122,14 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &returns) const; + // one backward pass of the dual ascent, fed with the mean entropy of the whole update: + // a scalar for the discrete coefficient, one entropy per action for the continuous ones + static void train_alpha( + const std::shared_ptr &alpha, + const std::shared_ptr &optim, + const std::shared_ptr &target_entropy, + const torch::Tensor &mean_entropy); + // GAE advantages (normalized over the valid pairs) and value targets, // computed with the pre-update critic GaeResult compute_gae(const PpoRollout &rollout, torch::Device device) const; diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index b6195c0f..d759b53a 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -50,8 +50,9 @@ namespace arenai::agent { vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, hidden_size_sensors, hidden_size_actions, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous(std::make_shared(0.001f)), - alpha_discrete(std::make_shared(0.001f)), + alpha_continuous( + std::make_shared(0.001f, 1e-5f, 1e-2f, nb_continuous_actions)), + alpha_discrete(std::make_shared(0.001f, 1e-5f, 1e-2f, 1)), continuous_target_entropy(std::make_shared( nb_continuous_actions, TARGET_SIGMA)), discrete_target_entropy( @@ -163,8 +164,7 @@ namespace arenai::agent { actor->act(state.vision, state.proprioception); const auto curr_continuous_action = truncated_normal_sample(curr_mu, curr_sigma); - const auto curr_continuous_entropy = - truncated_normal_entropy(curr_mu, curr_sigma).sum(-1, true); + const auto curr_continuous_entropy = truncated_normal_entropy(curr_mu, curr_sigma); const auto curr_discrete_entropy = multinomial_entropy(curr_discrete_proba); @@ -176,8 +176,8 @@ namespace arenai::agent { (curr_discrete_proba * torch::min(curr_q_values_1, curr_q_values_2)).sum(-1, true); const auto actor_loss = -torch::mean( - alpha_continuous->alpha().detach() * curr_continuous_entropy - + alpha_discrete->alpha().detach() * curr_discrete_entropy + q_value); + alpha_continuous->alpha().detach() * curr_continuous_entropy.sum(-1, true) + + alpha_discrete->alpha().detach() * curr_discrete_entropy.sum(-1, true) + q_value); actor_optim->zero_grad(); actor_loss.backward(); @@ -185,18 +185,26 @@ namespace arenai::agent { actor_optim->step(); // continuous entropy - const auto alpha_continuous_loss = torch::mean( - alpha_continuous->log_alpha() - * (curr_continuous_entropy.detach() - continuous_target_entropy->target_entropy())); + const auto alpha_continuous_loss = + torch::sum( + alpha_continuous->log_alpha() + * (curr_continuous_entropy.detach() + - continuous_target_entropy->target_entropy()), + -1) + .mean(); alpha_continuous_optim->zero_grad(); alpha_continuous_loss.backward(); alpha_continuous_optim->step(); // discrete entropy - const auto alpha_discrete_loss = torch::mean( - alpha_discrete->log_alpha() - * (curr_discrete_entropy.detach() - discrete_target_entropy->target_entropy())); + const auto alpha_discrete_loss = + torch::sum( + alpha_discrete->log_alpha() + * (curr_discrete_entropy.detach() + - discrete_target_entropy->target_entropy()), + -1) + .mean(); alpha_discrete_optim->zero_grad(); alpha_discrete_loss.backward(); @@ -214,8 +222,8 @@ namespace arenai::agent { critic_2_mean_loss_metric->add(critic_2_loss.cpu().item()); critic_2_std_loss_metric->add(critic_2_loss.cpu().item()); - alpha_continuous_metric->add(alpha_continuous->alpha().item()); - alpha_discrete_metric->add(alpha_discrete->alpha().item()); + alpha_continuous_metric->add(alpha_continuous->alpha().mean().item()); + alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); } continuous_target_entropy_metric->add( diff --git a/arenai_agent/src/agents/sac/sac_trainer.h b/arenai_agent/src/agents/sac/sac_trainer.h index 6dbd2e33..8de41dec 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.h +++ b/arenai_agent/src/agents/sac/sac_trainer.h @@ -48,8 +48,8 @@ namespace arenai::agent { std::shared_ptr target_critic_1; std::shared_ptr target_critic_2; - std::shared_ptr alpha_continuous; - std::shared_ptr alpha_discrete; + std::shared_ptr alpha_continuous; + std::shared_ptr alpha_discrete; std::shared_ptr continuous_target_entropy; std::shared_ptr discrete_target_entropy; diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index b70322f5..86ab690d 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -8,8 +8,8 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; - constexpr float TARGET_SIGMA = 0.1f; - constexpr float TARGET_FIRE_PROBABILITY = 0.05f; + constexpr float TARGET_SIGMA = 0.2f; + constexpr float TARGET_FIRE_PROBABILITY = 0.3f; constexpr float SIGMA_MIN = 1e-4f; constexpr float SIGMA_MAX = 2.f; diff --git a/arenai_agent/src/networks/entropy.cpp b/arenai_agent/src/networks/entropy.cpp index 191a5d97..bcf09b71 100644 --- a/arenai_agent/src/networks/entropy.cpp +++ b/arenai_agent/src/networks/entropy.cpp @@ -24,6 +24,25 @@ namespace arenai::agent { torch::Tensor AlphaParameter::alpha() { return log_alpha().exp(); } + MultiAlphaParameters::MultiAlphaParameters( + const float initial_alpha, const float min_alpha, const float max_alpha, + const int nb_alphas) + : log_alpha_tensor(register_parameter( + "log_alpha", + torch::full({nb_alphas}, std::log(std::clamp(initial_alpha, min_alpha, max_alpha))))), + min_log_alpha(std::log(min_alpha)), max_log_alpha(std::log(max_alpha)) {} + + torch::Tensor MultiAlphaParameters::log_alpha() { + { + const torch::NoGradGuard no_grad; + log_alpha_tensor.data().clamp_(min_log_alpha, max_log_alpha); + } + + return log_alpha_tensor; + } + + torch::Tensor MultiAlphaParameters::alpha() { return log_alpha().exp(); } + /* * Constant target entropy */ @@ -41,6 +60,10 @@ namespace arenai::agent { : ConstantTargetEntropy( truncated_normal_target_entropy(nb_continuous_action, target_sigma)) {} + ConstantContinuousPerActionTargetEntropy::ConstantContinuousPerActionTargetEntropy( + const float target_sigma) + : ConstantTargetEntropy(truncated_normal_target_entropy(1, target_sigma)) {} + /* * Target entropy warmup */ diff --git a/arenai_agent/src/networks/entropy.h b/arenai_agent/src/networks/entropy.h index 5d0367ce..8f4d1a0d 100644 --- a/arenai_agent/src/networks/entropy.h +++ b/arenai_agent/src/networks/entropy.h @@ -20,6 +20,30 @@ namespace arenai::agent { torch::Tensor log_alpha_tensor; }; + // one independent coefficient per action dimension: a single coefficient can only + // constrain the summed entropy, which a wide dimension keeps on target while another one + // collapses. + // The bounds are anti-windup guards: dual ascent is a pure integrator, so while the + // entropy stays on one side of its target log_alpha drifts without bound and ends up + // orders of magnitude away from any useful value, unable to come back in time once the + // error flips sign. + class MultiAlphaParameters final : public torch::nn::Module { + public: + MultiAlphaParameters(float initial_alpha, float min_alpha, float max_alpha, int nb_alphas); + + // clamps the parameters in place instead of returning a clamped view: torch::clamp + // has a zero gradient outside the bounds, so a clamped view would freeze log_alpha + // for good the first time it steps past a bound, in both directions + torch::Tensor log_alpha(); + torch::Tensor alpha(); + + private: + torch::Tensor log_alpha_tensor; + + float min_log_alpha; + float max_log_alpha; + }; + class AbstractTargetEntropy : public torch::nn::Module { public: virtual torch::Tensor target_entropy() = 0; @@ -49,6 +73,13 @@ namespace arenai::agent { explicit ConstantContinuousTargetEntropy(int nb_continuous_action, float target_sigma); }; + // target of a single action instead of the sum over them, to be broadcast against one + // alpha per dimension + class ConstantContinuousPerActionTargetEntropy : public ConstantTargetEntropy { + public: + explicit ConstantContinuousPerActionTargetEntropy(float target_sigma); + }; + /* * Warmup */ diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index 0f07da2e..d882cca3 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -32,7 +32,8 @@ std::unique_ptr PpoAgentTest::make_factory(const PpoTestCo .gae_lambda = 0.95f, .clip_epsilon = 0.2f, .grad_norm_max = 1.f, - .alpha_learning_rate = 1e-3f, + .continuous_alpha_learning_rate = 1e-3f, + .discrete_alpha_learning_rate = 1e-3f, .epochs = 1, .rollout_size = 8, .minibatch_size = 10}; @@ -76,7 +77,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 9); + ASSERT_EQ(metrics.size(), 15); } // ======================================================================== @@ -145,8 +146,9 @@ TEST_P(PpoSaveLoadParamTest, SaveCreatesExpectedFiles) { factory->get_trainer()->save(save_dir); const std::vector expected_files = { - "actor.pt", "critic.pt", "actor_optim.pt", - "critic_optim.pt", "actor_repr.txt", "critic_repr.txt", + "actor.pt", "critic.pt", "alpha_continuous.pt", "alpha_discrete.pt", + "actor_optim.pt", "critic_optim.pt", "alpha_continuous_optim.pt", "alpha_discrete_optim.pt", + "actor_repr.txt", "critic_repr.txt", }; for (const auto &f: expected_files) diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index e7e210c0..d0c22a70 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -21,7 +21,8 @@ PpoTrainingTest::make_factory(const PpoTrainingTestConfig &cfg) const { .gamma = 0.99f, .gae_lambda = 0.95f, .clip_epsilon = 0.2f, - .alpha_learning_rate = 1e-3f, + .continuous_alpha_learning_rate = 1e-3f, + .discrete_alpha_learning_rate = 1e-3f, .epochs = 2, .rollout_size = ROLLOUT_SIZE, .minibatch_size = MINIBATCH_SIZE}; @@ -76,7 +77,7 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { // target_kl = 0 : early stop disabled so every minibatch applies its update const auto trainer = std::make_shared( actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, - cfg.nb_continuous_actions, 1e-3f, 1e-3f, 1e-3f, 8, std::vector{16}, vision_channels, + cfg.nb_continuous_actions, 1e-3f, 1e-3f, 1e-3f, 1e-3f, 8, std::vector{16}, vision_channels, group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, 1.f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE); std::vector initial_parameters; From f38db5399a886136fd782bc5285692367ad1c2fc Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Sun, 16 Aug 2026 21:54:19 +0200 Subject: [PATCH 37/70] update target sigma and target fire proba + higher lr for alphas in ppo --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 12 +----------- arenai_agent/src/networks/constants.h | 4 ++-- 2 files changed, 3 insertions(+), 13 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 7f20d624..883b7120 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -32,17 +32,7 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - // lr of the SAC-style adaptive entropy alphas (fixed targets: sigma 0.2, fire proba 0.15). - // Plain SGD, so the step is lr * (entropy - target): proportional to the error, it slows - // down near the target instead of the fixed +-lr of a normalized optimizer, which drove - // the alpha into a limit cycle. Two rates because the two errors live on different - // scales: the continuous entropy is summed over the actions (error ~1 nat), the discrete - // one is a single binary entropy (error ~0.15 nat). - // Sized for reaction speed alone, not for windup: the MultiAlphaParameters bounds cap - // the drift whatever the rate. At these values an alpha crosses its whole admissible - // range in ~130 rollouts once the entropy leaves its target, against ~1300 an order of - // magnitude lower - too slow to catch an entropy collapse. - float continuous_alpha_learning_rate = 1e-2f; + float continuous_alpha_learning_rate = 4e-2f; float discrete_alpha_learning_rate = 1e-1f; int epochs = 4; int rollout_size = 30 * 30; diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index 86ab690d..b70322f5 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -8,8 +8,8 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; - constexpr float TARGET_SIGMA = 0.2f; - constexpr float TARGET_FIRE_PROBABILITY = 0.3f; + constexpr float TARGET_SIGMA = 0.1f; + constexpr float TARGET_FIRE_PROBABILITY = 0.05f; constexpr float SIGMA_MIN = 1e-4f; constexpr float SIGMA_MAX = 2.f; From 42e769fc0b04f70a4b3bab93cd5880106c9fd2e8 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 20:26:00 +0200 Subject: [PATCH 38/70] fix+refactor multi alpha --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 6 ++-- arenai_agent/src/agents/ppo/ppo_trainer.h | 6 ++-- arenai_agent/src/agents/sac/sac_trainer.cpp | 31 ++++++++--------- arenai_agent/src/agents/sac/sac_trainer.h | 4 +-- arenai_agent/src/networks/entropy.cpp | 33 +++++++++++-------- arenai_agent/src/networks/entropy.h | 26 +++++++-------- .../src/tests_networks/tests_entropy.cpp | 8 ++--- 7 files changed, 59 insertions(+), 55 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 606b10fd..ce3fdf32 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -49,8 +49,8 @@ namespace arenai::agent { // the bounds are anti-windup guards, two decades on each side of the initial value: // they stay inactive as long as the dual ascent behaves alpha_continuous( - std::make_shared(1e-3f, 1e-5f, 1e-2f, nb_continuous_actions)), - alpha_discrete(std::make_shared(1e-3f, 1e-5f, 1e-2f, 1)), + std::make_shared(1e-3f, 1e-5f, 1e-2f, nb_continuous_actions)), + alpha_discrete(std::make_shared(1e-3f, 1e-5f, 1e-2f, 1)), continuous_target_entropy( std::make_shared(TARGET_SIGMA)), discrete_target_entropy( @@ -279,7 +279,7 @@ namespace arenai::agent { } void PpoTrainer::train_alpha( - const std::shared_ptr &alpha, + const std::shared_ptr &alpha, const std::shared_ptr &optim, const std::shared_ptr &target_entropy, const torch::Tensor &mean_entropy) { diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 7019f8f0..0b7cac37 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -54,8 +54,8 @@ namespace arenai::agent { std::shared_ptr critic; // adaptive entropy coefficients (dual ascent toward fixed entropy targets) - std::shared_ptr alpha_continuous; - std::shared_ptr alpha_discrete; + std::shared_ptr alpha_continuous; + std::shared_ptr alpha_discrete; std::shared_ptr continuous_target_entropy; std::shared_ptr discrete_target_entropy; @@ -125,7 +125,7 @@ namespace arenai::agent { // one backward pass of the dual ascent, fed with the mean entropy of the whole update: // a scalar for the discrete coefficient, one entropy per action for the continuous ones static void train_alpha( - const std::shared_ptr &alpha, + const std::shared_ptr &alpha, const std::shared_ptr &optim, const std::shared_ptr &target_entropy, const torch::Tensor &mean_entropy); diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index d759b53a..4e0c5475 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -51,10 +51,10 @@ namespace arenai::agent { hidden_size_sensors, hidden_size_actions, critic_hidden_sizes, vision_channels, group_norm_nums)), alpha_continuous( - std::make_shared(0.001f, 1e-5f, 1e-2f, nb_continuous_actions)), - alpha_discrete(std::make_shared(0.001f, 1e-5f, 1e-2f, 1)), - continuous_target_entropy(std::make_shared( - nb_continuous_actions, TARGET_SIGMA)), + std::make_shared(0.001f, 1e-5f, 1e-2f, nb_continuous_actions)), + alpha_discrete(std::make_shared(0.001f, 1e-5f, 1e-2f, 1)), + continuous_target_entropy( + std::make_shared(1, TARGET_SIGMA)), discrete_target_entropy( std::make_shared(TARGET_FIRE_PROBABILITY)), actor_optim(std::make_unique( @@ -109,8 +109,7 @@ namespace arenai::agent { actor->act(next_state.vision, next_state.proprioception); const auto next_continuous_action = truncated_normal_sample(next_mu, next_sigma); - const auto next_continuous_entropy = - truncated_normal_entropy(next_mu, next_sigma).sum(-1, true); + const auto next_continuous_entropy = truncated_normal_entropy(next_mu, next_sigma); const auto next_discrete_entropy = multinomial_entropy(next_discrete_proba); @@ -119,14 +118,15 @@ namespace arenai::agent { const auto next_target_q_values_2 = target_critic_2->value_per_discrete_action( next_state.vision, next_state.proprioception, next_continuous_action); - const auto next_min_q_value = - (next_discrete_proba - * torch::min(next_target_q_values_1, next_target_q_values_2)) - .sum(-1, true); + const auto next_min_q_value = torch::sum( + next_discrete_proba + * torch::min(next_target_q_values_1, next_target_q_values_2), + -1, true); - const auto target_v_value = next_min_q_value - + alpha_continuous->alpha() * next_continuous_entropy - + alpha_discrete->alpha() * next_discrete_entropy; + const auto target_v_value = + next_min_q_value + + torch::sum(alpha_continuous->alpha() * next_continuous_entropy, -1, true) + + torch::sum(alpha_discrete->alpha() * next_discrete_entropy, -1, true); target_q_values = reward + (1.f - done.to(torch::kFloat)) * gamma * target_v_value; } @@ -176,8 +176,9 @@ namespace arenai::agent { (curr_discrete_proba * torch::min(curr_q_values_1, curr_q_values_2)).sum(-1, true); const auto actor_loss = -torch::mean( - alpha_continuous->alpha().detach() * curr_continuous_entropy.sum(-1, true) - + alpha_discrete->alpha().detach() * curr_discrete_entropy.sum(-1, true) + q_value); + torch::sum(alpha_continuous->alpha().detach() * curr_continuous_entropy, -1, true) + + torch::sum(alpha_discrete->alpha().detach() * curr_discrete_entropy, -1, true) + + q_value); actor_optim->zero_grad(); actor_loss.backward(); diff --git a/arenai_agent/src/agents/sac/sac_trainer.h b/arenai_agent/src/agents/sac/sac_trainer.h index 8de41dec..d96497e5 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.h +++ b/arenai_agent/src/agents/sac/sac_trainer.h @@ -48,8 +48,8 @@ namespace arenai::agent { std::shared_ptr target_critic_1; std::shared_ptr target_critic_2; - std::shared_ptr alpha_continuous; - std::shared_ptr alpha_discrete; + std::shared_ptr alpha_continuous; + std::shared_ptr alpha_discrete; std::shared_ptr continuous_target_entropy; std::shared_ptr discrete_target_entropy; diff --git a/arenai_agent/src/networks/entropy.cpp b/arenai_agent/src/networks/entropy.cpp index bcf09b71..76874060 100644 --- a/arenai_agent/src/networks/entropy.cpp +++ b/arenai_agent/src/networks/entropy.cpp @@ -16,33 +16,38 @@ using namespace arenai::agent; namespace arenai::agent { - AlphaParameter::AlphaParameter(const float initial_alpha) - : log_alpha_tensor( - register_parameter("log_alpha", torch::tensor({std::log(initial_alpha)}))) {} + /* + * Alpha parameters [0; +inf[ + */ + + AlphaParameters::AlphaParameters(const float initial_alpha, const int nb_alphas) + : log_alpha_tensor(register_parameter( + "log_alpha", + torch::tensor(std::vector(nb_alphas, std::log(initial_alpha))).unsqueeze(0))) {} - torch::Tensor AlphaParameter::log_alpha() { return log_alpha_tensor; } + torch::Tensor AlphaParameters::log_alpha() { return log_alpha_tensor; } - torch::Tensor AlphaParameter::alpha() { return log_alpha().exp(); } + torch::Tensor AlphaParameters::alpha() { return log_alpha().exp(); } - MultiAlphaParameters::MultiAlphaParameters( + /* + * Range alpha parameters + */ + + RangeAlphaParameters::RangeAlphaParameters( const float initial_alpha, const float min_alpha, const float max_alpha, const int nb_alphas) - : log_alpha_tensor(register_parameter( - "log_alpha", - torch::full({nb_alphas}, std::log(std::clamp(initial_alpha, min_alpha, max_alpha))))), + : AlphaParameters(std::clamp(initial_alpha, min_alpha, max_alpha), nb_alphas), min_log_alpha(std::log(min_alpha)), max_log_alpha(std::log(max_alpha)) {} - torch::Tensor MultiAlphaParameters::log_alpha() { + torch::Tensor RangeAlphaParameters::log_alpha() { { const torch::NoGradGuard no_grad; - log_alpha_tensor.data().clamp_(min_log_alpha, max_log_alpha); + AlphaParameters::log_alpha().data().clamp_(min_log_alpha, max_log_alpha); } - return log_alpha_tensor; + return AlphaParameters::log_alpha(); } - torch::Tensor MultiAlphaParameters::alpha() { return log_alpha().exp(); } - /* * Constant target entropy */ diff --git a/arenai_agent/src/networks/entropy.h b/arenai_agent/src/networks/entropy.h index 8f4d1a0d..f410ea55 100644 --- a/arenai_agent/src/networks/entropy.h +++ b/arenai_agent/src/networks/entropy.h @@ -9,17 +9,6 @@ namespace arenai::agent { - class AlphaParameter final : public torch::nn::Module { - public: - explicit AlphaParameter(float initial_alpha); - - torch::Tensor log_alpha(); - torch::Tensor alpha(); - - private: - torch::Tensor log_alpha_tensor; - }; - // one independent coefficient per action dimension: a single coefficient can only // constrain the summed entropy, which a wide dimension keeps on target while another one // collapses. @@ -27,19 +16,28 @@ namespace arenai::agent { // entropy stays on one side of its target log_alpha drifts without bound and ends up // orders of magnitude away from any useful value, unable to come back in time once the // error flips sign. - class MultiAlphaParameters final : public torch::nn::Module { + class AlphaParameters : public torch::nn::Module { public: - MultiAlphaParameters(float initial_alpha, float min_alpha, float max_alpha, int nb_alphas); + explicit AlphaParameters(float initial_alpha, int nb_alphas); // clamps the parameters in place instead of returning a clamped view: torch::clamp // has a zero gradient outside the bounds, so a clamped view would freeze log_alpha // for good the first time it steps past a bound, in both directions - torch::Tensor log_alpha(); + virtual torch::Tensor log_alpha(); torch::Tensor alpha(); private: torch::Tensor log_alpha_tensor; + }; + + class RangeAlphaParameters final : public AlphaParameters { + public: + explicit RangeAlphaParameters( + float initial_alpha, float min_alpha, float max_alpha, int nb_alphas); + + torch::Tensor log_alpha() override; + private: float min_log_alpha; float max_log_alpha; }; diff --git a/arenai_agent/tests/src/tests_networks/tests_entropy.cpp b/arenai_agent/tests/src/tests_networks/tests_entropy.cpp index e4a7c0f5..cb6aae4f 100644 --- a/arenai_agent/tests/src/tests_networks/tests_entropy.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_entropy.cpp @@ -11,7 +11,7 @@ using namespace arenai::agent; TEST_F(AlphaParameterTest, AlphaAlwaysPositive) { for (const float init: {0.01f, 0.1f, 1.0f, 10.0f}) { - AlphaParameter param(init); + AlphaParameters param(init, 1); ASSERT_GT(param.alpha().item(), 0.0f) << "alpha should be positive for initial_alpha=" << init; } @@ -19,19 +19,19 @@ TEST_F(AlphaParameterTest, AlphaAlwaysPositive) { TEST_F(AlphaParameterTest, InitialValueMatchesInput) { constexpr float initial = 0.2f; - AlphaParameter param(initial); + AlphaParameters param(initial, 1); ASSERT_NEAR(param.alpha().item(), initial, 1e-6f); } TEST_F(AlphaParameterTest, LogAlphaRequiresGrad) { - AlphaParameter param(1.0f); + AlphaParameters param(1.0f, 1); ASSERT_TRUE(param.log_alpha().requires_grad()); } TEST_F(AlphaParameterTest, LogAlphaConsistentWithAlpha) { - AlphaParameter param(0.5f); + AlphaParameters param(0.5f, 1); const auto log_a = param.log_alpha().item(); const auto a = param.alpha().item(); From 4fffd25a63cc026dc9e74ce1fd1386db77d7c140 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 21:52:06 +0200 Subject: [PATCH 39/70] fix continuous target entropy depending on mu (compute entropy per batch item with its mu and the target sigma, then mean over batch) --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 88 +++++++++++---------- arenai_agent/src/agents/ppo/ppo_trainer.h | 13 +-- arenai_agent/src/agents/sac/sac_trainer.cpp | 35 ++++---- arenai_agent/src/agents/sac/sac_trainer.h | 8 +- 4 files changed, 71 insertions(+), 73 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index ce3fdf32..7480ad7b 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -51,10 +51,6 @@ namespace arenai::agent { alpha_continuous( std::make_shared(1e-3f, 1e-5f, 1e-2f, nb_continuous_actions)), alpha_discrete(std::make_shared(1e-3f, 1e-5f, 1e-2f, 1)), - continuous_target_entropy( - std::make_shared(TARGET_SIGMA)), - discrete_target_entropy( - std::make_shared(TARGET_FIRE_PROBABILITY)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_optim(std::make_unique( @@ -83,7 +79,11 @@ namespace arenai::agent { kl_metric(std::make_shared("kl", metric_window_size, 2, true)), gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), grad_norm_max(grad_norm_max), epochs(epochs), rollout_size(rollout_size), - minibatch_size(minibatch_size) { + minibatch_size(minibatch_size), + target_sigma( + torch::tensor(std::vector(nb_continuous_actions, TARGET_SIGMA)).unsqueeze(0)), + target_discrete_entropy( + torch::tensor(multinomial_target_entropy(TARGET_FIRE_PROBABILITY))) { to(device); } @@ -125,8 +125,14 @@ namespace arenai::agent { // one running sum per continuous action; left undefined until the first minibatch so // the action count does not have to be carried around - torch::Tensor continuous_entropy_sum; - float discrete_entropy_sum = 0.f; + const auto options = torch::TensorOptions().device(device); + torch::Tensor continuous_entropy_sum = + torch::zeros({flat_continuous_actions.size(-1)}, options); + torch::Tensor continuous_target_entropy_sum = + torch::zeros({flat_continuous_actions.size(-1)}, options); + torch::Tensor discrete_entropy_sum = + torch::zeros({flat_discrete_actions.size(-1)}, options); + int nb_actor_minibatches = 0; bool kl_stop = false; @@ -144,15 +150,18 @@ namespace arenai::agent { // actor: skipped for the rest of the update once a minibatch drifted past // the KL threshold if (!kl_stop) { - const auto [kl_exceeded, continuous_entropy, discrete_entropy] = train_actor( - mb_vision, mb_proprioception, select(flat_continuous_actions, idx), - select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), - select(flat_advantages, idx)); - - continuous_entropy_sum = continuous_entropy_sum.defined() - ? continuous_entropy_sum + continuous_entropy - : continuous_entropy; + const auto + [kl_exceeded, continuous_entropy, continuous_target_entropy, + discrete_entropy] = + train_actor( + mb_vision, mb_proprioception, select(flat_continuous_actions, idx), + select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), + select(flat_advantages, idx)); + + continuous_entropy_sum += continuous_entropy; + continuous_target_entropy_sum += continuous_target_entropy; discrete_entropy_sum += discrete_entropy; + nb_actor_minibatches++; kl_stop = kl_exceeded; @@ -169,25 +178,23 @@ namespace arenai::agent { if (nb_actor_minibatches > 0) { const auto nb_minibatches = static_cast(nb_actor_minibatches); + const auto continuous_target_entropy = continuous_target_entropy_sum / nb_minibatches; + train_alpha( alpha_continuous, alpha_continuous_optim, continuous_target_entropy, continuous_entropy_sum / nb_minibatches); train_alpha( - alpha_discrete, alpha_discrete_optim, discrete_target_entropy, - torch::tensor( - discrete_entropy_sum / nb_minibatches, - torch::TensorOptions().device(continuous_entropy_sum.device()))); + alpha_discrete, alpha_discrete_optim, target_discrete_entropy, + discrete_entropy_sum / nb_minibatches); const auto continuous_alphas = alpha_continuous->alpha(); alpha_continuous_metric->add(continuous_alphas.mean().item()); alpha_continuous_max_metric->add(continuous_alphas.max().item()); alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); - } - continuous_target_entropy_metric->add( - continuous_target_entropy->target_entropy().item()); - discrete_target_entropy_metric->add( - discrete_target_entropy->target_entropy().item()); + continuous_target_entropy_metric->add(continuous_target_entropy.mean().item()); + discrete_target_entropy_metric->add(target_discrete_entropy.item()); + } } ActorUpdateResult PpoTrainer::train_actor( @@ -213,11 +220,15 @@ namespace arenai::agent { const auto continuous_entropy = truncated_normal_entropy(mu, sigma); const auto discrete_entropy = multinomial_entropy(discrete_proba); + const auto continuous_target_entropy = truncated_normal_entropy(mu, target_sigma); + + // mean over batch const auto mean_continuous_entropy = continuous_entropy.mean(0).detach(); - const auto mean_discrete_entropy = discrete_entropy.mean().item(); + const auto mean_continuous_target_entropy = continuous_target_entropy.mean(0).detach(); + const auto mean_discrete_entropy = discrete_entropy.mean(0).detach(); continuous_entropy_metric->add(mean_continuous_entropy.mean().item()); - discrete_entropy_metric->add(mean_discrete_entropy); + discrete_entropy_metric->add(mean_discrete_entropy.mean().item()); sigma_metric->add(sigma.mean().item()); sigma_min_metric->add(sigma.mean(0).min().item()); @@ -229,6 +240,7 @@ namespace arenai::agent { return { .kl_exceeded = true, .continuous_entropy = mean_continuous_entropy, + .continuous_target_entropy = mean_continuous_target_entropy, .discrete_entropy = mean_discrete_entropy}; const auto clipped_ratio = torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); @@ -237,7 +249,7 @@ namespace arenai::agent { // one coefficient per action dimension, broadcast over the batch and summed back const auto continuous_entropy_bonus = - (alpha_continuous->alpha().detach() * continuous_entropy).sum(-1, true); + torch::sum(alpha_continuous->alpha().detach() * continuous_entropy, -1, true); const auto actor_loss = -torch::mean( surrogate + continuous_entropy_bonus @@ -258,6 +270,7 @@ namespace arenai::agent { return { .kl_exceeded = false, .continuous_entropy = mean_continuous_entropy, + .continuous_target_entropy = mean_continuous_target_entropy, .discrete_entropy = mean_discrete_entropy}; } @@ -280,18 +293,10 @@ namespace arenai::agent { void PpoTrainer::train_alpha( const std::shared_ptr &alpha, - const std::shared_ptr &optim, - const std::shared_ptr &target_entropy, + const std::shared_ptr &optim, const torch::Tensor &target_entropy, const torch::Tensor &mean_entropy) { - // entropy above target -> alpha decays toward 0 (stops pushing sigma up); - // entropy below target -> alpha grows (prevents entropy collapse). - // SGD, not Adam: the step must stay proportional to the entropy error, a normalized - // step keeps its full speed next to the target and makes the loop oscillate. - // Summed and not averaged: each coefficient must receive its own error untouched, - // averaging would divide every gradient by the number of dimensions - const auto alpha_loss = - torch::sum(alpha->log_alpha() * (mean_entropy - target_entropy->target_entropy())); + const auto alpha_loss = torch::sum(alpha->log_alpha() * (mean_entropy - target_entropy)); optim->zero_grad(); alpha_loss.backward(); @@ -408,20 +413,17 @@ namespace arenai::agent { alpha_continuous->train(train); alpha_discrete->train(train); - - continuous_target_entropy->train(train); - discrete_target_entropy->train(train); } - void PpoTrainer::to(const torch::Device device) const { + void PpoTrainer::to(const torch::Device device) { actor->to(device); critic->to(device); alpha_continuous->to(device); alpha_discrete->to(device); - continuous_target_entropy->to(device); - discrete_target_entropy->to(device); + target_sigma = target_sigma.to(device); + target_discrete_entropy = target_discrete_entropy.to(device); } int PpoTrainer::count_parameters() { diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 0b7cac37..2c7d6c94 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -23,7 +23,8 @@ namespace arenai::agent { bool kl_exceeded; // one entropy per continuous action, detached: each dimension feeds its own alpha torch::Tensor continuous_entropy; - float discrete_entropy; + torch::Tensor continuous_target_entropy; + torch::Tensor discrete_entropy; }; class PpoTrainer final : public AbstractTrainer { @@ -56,8 +57,6 @@ namespace arenai::agent { // adaptive entropy coefficients (dual ascent toward fixed entropy targets) std::shared_ptr alpha_continuous; std::shared_ptr alpha_discrete; - std::shared_ptr continuous_target_entropy; - std::shared_ptr discrete_target_entropy; std::shared_ptr actor_optim; std::shared_ptr critic_optim; @@ -107,6 +106,9 @@ namespace arenai::agent { // rows per gradient step; also the chunk size for the no-grad value evaluation int minibatch_size; + torch::Tensor target_sigma; + torch::Tensor target_discrete_entropy; + void train() const; // one backward pass on the actor for a single minibatch; the update is skipped @@ -126,8 +128,7 @@ namespace arenai::agent { // a scalar for the discrete coefficient, one entropy per action for the continuous ones static void train_alpha( const std::shared_ptr &alpha, - const std::shared_ptr &optim, - const std::shared_ptr &target_entropy, + const std::shared_ptr &optim, const torch::Tensor &target_entropy, const torch::Tensor &mean_entropy); // GAE advantages (normalized over the valid pairs) and value targets, @@ -135,7 +136,7 @@ namespace arenai::agent { GaeResult compute_gae(const PpoRollout &rollout, torch::Device device) const; void set_train(bool train) const; - void to(torch::Device device) const; + void to(torch::Device device); }; }// namespace arenai::agent diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 4e0c5475..d365b8c3 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -53,10 +53,6 @@ namespace arenai::agent { alpha_continuous( std::make_shared(0.001f, 1e-5f, 1e-2f, nb_continuous_actions)), alpha_discrete(std::make_shared(0.001f, 1e-5f, 1e-2f, 1)), - continuous_target_entropy( - std::make_shared(1, TARGET_SIGMA)), - discrete_target_entropy( - std::make_shared(TARGET_FIRE_PROBABILITY)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_1_optim(std::make_unique( @@ -80,7 +76,11 @@ namespace arenai::agent { continuous_target_entropy_metric(std::make_shared("Hc_t")), discrete_target_entropy_metric(std::make_shared("Hd_t")), tau(tau), gamma(gamma), train_every(train_every), train_counter(0), epochs(epochs), - batch_size(batch_size) { + batch_size(batch_size), + target_sigma( + torch::tensor(std::vector(nb_continuous_actions, TARGET_SIGMA)).unsqueeze(0)), + target_discrete_entropy( + torch::tensor(multinomial_target_entropy(TARGET_FIRE_PROBABILITY))) { hard_update(target_critic_1, critic_1); hard_update(target_critic_2, critic_2); @@ -186,11 +186,12 @@ namespace arenai::agent { actor_optim->step(); // continuous entropy + const auto continuous_target_entropy = truncated_normal_entropy(curr_mu, target_sigma); + const auto alpha_continuous_loss = torch::sum( alpha_continuous->log_alpha() - * (curr_continuous_entropy.detach() - - continuous_target_entropy->target_entropy()), + * torch::detach(curr_continuous_entropy - continuous_target_entropy), -1) .mean(); @@ -202,8 +203,7 @@ namespace arenai::agent { const auto alpha_discrete_loss = torch::sum( alpha_discrete->log_alpha() - * (curr_discrete_entropy.detach() - - discrete_target_entropy->target_entropy()), + * (curr_discrete_entropy.detach() - target_discrete_entropy), -1) .mean(); @@ -218,6 +218,9 @@ namespace arenai::agent { continuous_entropy_metric->add(curr_continuous_entropy.mean().item()); discrete_entropy_metric->add(curr_discrete_entropy.mean().item()); + continuous_target_entropy_metric->add(continuous_target_entropy.mean().item()); + discrete_target_entropy_metric->add(target_discrete_entropy.item()); + critic_1_mean_loss_metric->add(critic_1_loss.cpu().item()); critic_1_std_loss_metric->add(critic_1_loss.cpu().item()); critic_2_mean_loss_metric->add(critic_2_loss.cpu().item()); @@ -226,11 +229,6 @@ namespace arenai::agent { alpha_continuous_metric->add(alpha_continuous->alpha().mean().item()); alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); } - - continuous_target_entropy_metric->add( - continuous_target_entropy->target_entropy().item()); - discrete_target_entropy_metric->add( - discrete_target_entropy->target_entropy().item()); } std::vector> SacTrainer::get_metrics() { @@ -286,15 +284,12 @@ namespace arenai::agent { alpha_continuous->train(train); alpha_discrete->train(train); - continuous_target_entropy->train(train); - discrete_target_entropy->train(train); - // force eval for target critics target_critic_1->train(false); target_critic_2->train(false); } - void SacTrainer::to(const torch::Device device) const { + void SacTrainer::to(const torch::Device device) { actor->to(device); critic_1->to(device); @@ -306,8 +301,8 @@ namespace arenai::agent { alpha_continuous->to(device); alpha_discrete->to(device); - continuous_target_entropy->to(device); - discrete_target_entropy->to(device); + target_sigma = target_sigma.to(device); + target_discrete_entropy = target_discrete_entropy.to(device); } int SacTrainer::count_parameters() { diff --git a/arenai_agent/src/agents/sac/sac_trainer.h b/arenai_agent/src/agents/sac/sac_trainer.h index d96497e5..2b6f3d5a 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.h +++ b/arenai_agent/src/agents/sac/sac_trainer.h @@ -51,9 +51,6 @@ namespace arenai::agent { std::shared_ptr alpha_continuous; std::shared_ptr alpha_discrete; - std::shared_ptr continuous_target_entropy; - std::shared_ptr discrete_target_entropy; - std::shared_ptr actor_optim; std::shared_ptr critic_1_optim; std::shared_ptr critic_2_optim; @@ -88,10 +85,13 @@ namespace arenai::agent { int epochs; int batch_size; + torch::Tensor target_sigma; + torch::Tensor target_discrete_entropy; + void train() const; void set_train(bool train) const; - void to(torch::Device device) const; + void to(torch::Device device); }; }// namespace arenai::agent From ba2b61bae2b36f93dce3c51174d00fba7a30465d Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 22:03:27 +0200 Subject: [PATCH 40/70] rename variable --- arenai_agent/src/agents/sac/sac_trainer.cpp | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index d365b8c3..780e5891 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -172,8 +172,8 @@ namespace arenai::agent { state.vision, state.proprioception, curr_continuous_action); const auto curr_q_values_2 = critic_2->value_per_discrete_action( state.vision, state.proprioception, curr_continuous_action); - const auto q_value = - (curr_discrete_proba * torch::min(curr_q_values_1, curr_q_values_2)).sum(-1, true); + const auto q_value = torch::sum( + curr_discrete_proba * torch::min(curr_q_values_1, curr_q_values_2), -1, true); const auto actor_loss = -torch::mean( torch::sum(alpha_continuous->alpha().detach() * curr_continuous_entropy, -1, true) @@ -186,12 +186,13 @@ namespace arenai::agent { actor_optim->step(); // continuous entropy - const auto continuous_target_entropy = truncated_normal_entropy(curr_mu, target_sigma); + const auto curr_continuous_target_entropy = + truncated_normal_entropy(curr_mu, target_sigma); const auto alpha_continuous_loss = torch::sum( alpha_continuous->log_alpha() - * torch::detach(curr_continuous_entropy - continuous_target_entropy), + * torch::detach(curr_continuous_entropy - curr_continuous_target_entropy), -1) .mean(); @@ -203,7 +204,7 @@ namespace arenai::agent { const auto alpha_discrete_loss = torch::sum( alpha_discrete->log_alpha() - * (curr_discrete_entropy.detach() - target_discrete_entropy), + * torch::detach(curr_discrete_entropy - target_discrete_entropy), -1) .mean(); @@ -218,7 +219,8 @@ namespace arenai::agent { continuous_entropy_metric->add(curr_continuous_entropy.mean().item()); discrete_entropy_metric->add(curr_discrete_entropy.mean().item()); - continuous_target_entropy_metric->add(continuous_target_entropy.mean().item()); + continuous_target_entropy_metric->add( + curr_continuous_target_entropy.mean().item()); discrete_target_entropy_metric->add(target_discrete_entropy.item()); critic_1_mean_loss_metric->add(critic_1_loss.cpu().item()); From 4f51da468461f9e705ab2c3bf36e93162228cea6 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 22:10:32 +0200 Subject: [PATCH 41/70] refactor --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 7480ad7b..a686b7ae 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -210,7 +210,7 @@ namespace arenai::agent { const auto clamped_proba = torch::clamp(discrete_proba, EPSILON, 1.0 - EPSILON); const auto curr_discrete_log_probs = - (discrete_actions * torch::log(clamped_proba)).sum(-1, true); + torch::sum(discrete_actions * torch::log(clamped_proba), -1, true); const auto ratio = torch::exp(curr_continuous_log_probs + curr_discrete_log_probs - old_log_probs); @@ -235,7 +235,9 @@ namespace arenai::agent { // approx KL (Schulman): E[(ratio - 1) - log ratio]; skip this minibatch when the // policy drifted too far from the rollout policy const auto approx_kl = torch::mean(ratio - 1.f - torch::log(ratio)).item(); + kl_metric->add(approx_kl); + if (target_kl > 0.f && approx_kl > 1.5f * target_kl) return { .kl_exceeded = true, From 5e26ce677d96face64c1f78176c2ac4338f26a81 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 22:19:02 +0200 Subject: [PATCH 42/70] refactor --- arenai_agent/src/networks/entropy.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/arenai_agent/src/networks/entropy.cpp b/arenai_agent/src/networks/entropy.cpp index 76874060..888d98da 100644 --- a/arenai_agent/src/networks/entropy.cpp +++ b/arenai_agent/src/networks/entropy.cpp @@ -40,12 +40,14 @@ namespace arenai::agent { min_log_alpha(std::log(min_alpha)), max_log_alpha(std::log(max_alpha)) {} torch::Tensor RangeAlphaParameters::log_alpha() { + auto curr_log_alpha = AlphaParameters::log_alpha(); + { const torch::NoGradGuard no_grad; - AlphaParameters::log_alpha().data().clamp_(min_log_alpha, max_log_alpha); + curr_log_alpha = curr_log_alpha.clamp(min_log_alpha, max_log_alpha); } - return AlphaParameters::log_alpha(); + return curr_log_alpha; } /* From 15ef7adbdae299d60d4f954245a6c039aabb0947 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 22:25:04 +0200 Subject: [PATCH 43/70] rename class (clamped alphas) --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 6 +++--- arenai_agent/src/agents/ppo/ppo_trainer.h | 6 +++--- arenai_agent/src/agents/sac/sac_trainer.cpp | 6 +++--- arenai_agent/src/agents/sac/sac_trainer.h | 4 ++-- arenai_agent/src/networks/entropy.cpp | 8 ++++---- arenai_agent/src/networks/entropy.h | 4 ++-- 6 files changed, 17 insertions(+), 17 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index a686b7ae..3ba1c5ff 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -49,8 +49,8 @@ namespace arenai::agent { // the bounds are anti-windup guards, two decades on each side of the initial value: // they stay inactive as long as the dual ascent behaves alpha_continuous( - std::make_shared(1e-3f, 1e-5f, 1e-2f, nb_continuous_actions)), - alpha_discrete(std::make_shared(1e-3f, 1e-5f, 1e-2f, 1)), + std::make_shared(1e-3f, 1e-5f, 1e-2f, nb_continuous_actions)), + alpha_discrete(std::make_shared(1e-3f, 1e-5f, 1e-2f, 1)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_optim(std::make_unique( @@ -294,7 +294,7 @@ namespace arenai::agent { } void PpoTrainer::train_alpha( - const std::shared_ptr &alpha, + const std::shared_ptr &alpha, const std::shared_ptr &optim, const torch::Tensor &target_entropy, const torch::Tensor &mean_entropy) { diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 2c7d6c94..9bce20ea 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -55,8 +55,8 @@ namespace arenai::agent { std::shared_ptr critic; // adaptive entropy coefficients (dual ascent toward fixed entropy targets) - std::shared_ptr alpha_continuous; - std::shared_ptr alpha_discrete; + std::shared_ptr alpha_continuous; + std::shared_ptr alpha_discrete; std::shared_ptr actor_optim; std::shared_ptr critic_optim; @@ -127,7 +127,7 @@ namespace arenai::agent { // one backward pass of the dual ascent, fed with the mean entropy of the whole update: // a scalar for the discrete coefficient, one entropy per action for the continuous ones static void train_alpha( - const std::shared_ptr &alpha, + const std::shared_ptr &alpha, const std::shared_ptr &optim, const torch::Tensor &target_entropy, const torch::Tensor &mean_entropy); diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 780e5891..f4f06c4c 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -50,9 +50,9 @@ namespace arenai::agent { vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, hidden_size_sensors, hidden_size_actions, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous( - std::make_shared(0.001f, 1e-5f, 1e-2f, nb_continuous_actions)), - alpha_discrete(std::make_shared(0.001f, 1e-5f, 1e-2f, 1)), + alpha_continuous(std::make_shared( + 0.001f, 1e-5f, 1e-2f, nb_continuous_actions)), + alpha_discrete(std::make_shared(0.001f, 1e-5f, 1e-2f, 1)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_1_optim(std::make_unique( diff --git a/arenai_agent/src/agents/sac/sac_trainer.h b/arenai_agent/src/agents/sac/sac_trainer.h index 2b6f3d5a..55d9115c 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.h +++ b/arenai_agent/src/agents/sac/sac_trainer.h @@ -48,8 +48,8 @@ namespace arenai::agent { std::shared_ptr target_critic_1; std::shared_ptr target_critic_2; - std::shared_ptr alpha_continuous; - std::shared_ptr alpha_discrete; + std::shared_ptr alpha_continuous; + std::shared_ptr alpha_discrete; std::shared_ptr actor_optim; std::shared_ptr critic_1_optim; diff --git a/arenai_agent/src/networks/entropy.cpp b/arenai_agent/src/networks/entropy.cpp index 888d98da..9dcd2af8 100644 --- a/arenai_agent/src/networks/entropy.cpp +++ b/arenai_agent/src/networks/entropy.cpp @@ -30,21 +30,21 @@ namespace arenai::agent { torch::Tensor AlphaParameters::alpha() { return log_alpha().exp(); } /* - * Range alpha parameters + * Clamped alpha parameters */ - RangeAlphaParameters::RangeAlphaParameters( + ClampedAlphaParameters::ClampedAlphaParameters( const float initial_alpha, const float min_alpha, const float max_alpha, const int nb_alphas) : AlphaParameters(std::clamp(initial_alpha, min_alpha, max_alpha), nb_alphas), min_log_alpha(std::log(min_alpha)), max_log_alpha(std::log(max_alpha)) {} - torch::Tensor RangeAlphaParameters::log_alpha() { + torch::Tensor ClampedAlphaParameters::log_alpha() { auto curr_log_alpha = AlphaParameters::log_alpha(); { const torch::NoGradGuard no_grad; - curr_log_alpha = curr_log_alpha.clamp(min_log_alpha, max_log_alpha); + curr_log_alpha.data().clamp_(min_log_alpha, max_log_alpha); } return curr_log_alpha; diff --git a/arenai_agent/src/networks/entropy.h b/arenai_agent/src/networks/entropy.h index f410ea55..dea0c443 100644 --- a/arenai_agent/src/networks/entropy.h +++ b/arenai_agent/src/networks/entropy.h @@ -30,9 +30,9 @@ namespace arenai::agent { torch::Tensor log_alpha_tensor; }; - class RangeAlphaParameters final : public AlphaParameters { + class ClampedAlphaParameters final : public AlphaParameters { public: - explicit RangeAlphaParameters( + explicit ClampedAlphaParameters( float initial_alpha, float min_alpha, float max_alpha, int nb_alphas); torch::Tensor log_alpha() override; From 43fc473e91e41dc9a53d1c12db9b4cf82b0479cd Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 22:53:43 +0200 Subject: [PATCH 44/70] fix entropy in PPO and SAC --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 57 ++++++++++++--------- arenai_agent/src/agents/ppo/ppo_trainer.h | 9 ++-- arenai_agent/src/agents/sac/sac_trainer.cpp | 2 +- 3 files changed, 37 insertions(+), 31 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 3ba1c5ff..c41a88e2 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -83,7 +83,7 @@ namespace arenai::agent { target_sigma( torch::tensor(std::vector(nb_continuous_actions, TARGET_SIGMA)).unsqueeze(0)), target_discrete_entropy( - torch::tensor(multinomial_target_entropy(TARGET_FIRE_PROBABILITY))) { + torch::tensor({multinomial_target_entropy(TARGET_FIRE_PROBABILITY)})) { to(device); } @@ -130,8 +130,7 @@ namespace arenai::agent { torch::zeros({flat_continuous_actions.size(-1)}, options); torch::Tensor continuous_target_entropy_sum = torch::zeros({flat_continuous_actions.size(-1)}, options); - torch::Tensor discrete_entropy_sum = - torch::zeros({flat_discrete_actions.size(-1)}, options); + torch::Tensor discrete_entropy_sum = torch::zeros({1}, options); int nb_actor_minibatches = 0; @@ -178,22 +177,12 @@ namespace arenai::agent { if (nb_actor_minibatches > 0) { const auto nb_minibatches = static_cast(nb_actor_minibatches); + const auto continuous_entropy = continuous_entropy_sum / nb_minibatches; const auto continuous_target_entropy = continuous_target_entropy_sum / nb_minibatches; - train_alpha( - alpha_continuous, alpha_continuous_optim, continuous_target_entropy, - continuous_entropy_sum / nb_minibatches); - train_alpha( - alpha_discrete, alpha_discrete_optim, target_discrete_entropy, - discrete_entropy_sum / nb_minibatches); + const auto discrete_entropy = discrete_entropy_sum / nb_minibatches; - const auto continuous_alphas = alpha_continuous->alpha(); - alpha_continuous_metric->add(continuous_alphas.mean().item()); - alpha_continuous_max_metric->add(continuous_alphas.max().item()); - alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); - - continuous_target_entropy_metric->add(continuous_target_entropy.mean().item()); - discrete_target_entropy_metric->add(target_discrete_entropy.item()); + train_alphas(continuous_entropy, continuous_target_entropy, discrete_entropy); } } @@ -293,16 +282,36 @@ namespace arenai::agent { critic_std_loss_metric->add(loss_value); } - void PpoTrainer::train_alpha( - const std::shared_ptr &alpha, - const std::shared_ptr &optim, const torch::Tensor &target_entropy, - const torch::Tensor &mean_entropy) { + void PpoTrainer::train_alphas( + const torch::Tensor &continuous_entropy, const torch::Tensor &continuous_target_entropy, + const torch::Tensor &discrete_entropy) const { + + // train continuous alpha + const auto continuous_alpha_loss = torch::sum( + alpha_continuous->log_alpha().squeeze(0) + * torch::detach(continuous_entropy - continuous_target_entropy)); + + alpha_continuous_optim->zero_grad(); + continuous_alpha_loss.backward(); + alpha_continuous_optim->step(); + + // train discrete alpha + const auto discrete_alpha_loss = torch::mean( + alpha_discrete->log_alpha().squeeze(0) + * torch::detach(discrete_entropy - target_discrete_entropy)); + + alpha_discrete_optim->zero_grad(); + discrete_alpha_loss.backward(); + alpha_discrete_optim->step(); - const auto alpha_loss = torch::sum(alpha->log_alpha() * (mean_entropy - target_entropy)); + // metrics + const auto continuous_alphas = alpha_continuous->alpha(); + alpha_continuous_metric->add(continuous_alphas.mean().item()); + alpha_continuous_max_metric->add(continuous_alphas.max().item()); + alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); - optim->zero_grad(); - alpha_loss.backward(); - optim->step(); + continuous_target_entropy_metric->add(continuous_target_entropy.mean().item()); + discrete_target_entropy_metric->add(target_discrete_entropy.item()); } GaeResult PpoTrainer::compute_gae(const PpoRollout &rollout, const torch::Device device) const { diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 9bce20ea..5a91342d 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -124,12 +124,9 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &returns) const; - // one backward pass of the dual ascent, fed with the mean entropy of the whole update: - // a scalar for the discrete coefficient, one entropy per action for the continuous ones - static void train_alpha( - const std::shared_ptr &alpha, - const std::shared_ptr &optim, const torch::Tensor &target_entropy, - const torch::Tensor &mean_entropy); + void train_alphas( + const torch::Tensor &continuous_entropy, const torch::Tensor &continuous_target_entropy, + const torch::Tensor &discrete_entropy) const; // GAE advantages (normalized over the valid pairs) and value targets, // computed with the pre-update critic diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index f4f06c4c..bc2133a6 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -80,7 +80,7 @@ namespace arenai::agent { target_sigma( torch::tensor(std::vector(nb_continuous_actions, TARGET_SIGMA)).unsqueeze(0)), target_discrete_entropy( - torch::tensor(multinomial_target_entropy(TARGET_FIRE_PROBABILITY))) { + torch::tensor({multinomial_target_entropy(TARGET_FIRE_PROBABILITY)}).unsqueeze(0)) { hard_update(target_critic_1, critic_1); hard_update(target_critic_2, critic_2); From c35e453767ac85fa56dec0be9a53edf22f358e61 Mon Sep 17 00:00:00 2001 From: samuel Date: Sun, 16 Aug 2026 23:04:09 +0200 Subject: [PATCH 45/70] harmonize PPO entropy shape like SAC --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index c41a88e2..d9178fcc 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -83,7 +83,7 @@ namespace arenai::agent { target_sigma( torch::tensor(std::vector(nb_continuous_actions, TARGET_SIGMA)).unsqueeze(0)), target_discrete_entropy( - torch::tensor({multinomial_target_entropy(TARGET_FIRE_PROBABILITY)})) { + torch::tensor({multinomial_target_entropy(TARGET_FIRE_PROBABILITY)}).unsqueeze(0)) { to(device); } @@ -127,10 +127,10 @@ namespace arenai::agent { // the action count does not have to be carried around const auto options = torch::TensorOptions().device(device); torch::Tensor continuous_entropy_sum = - torch::zeros({flat_continuous_actions.size(-1)}, options); + torch::zeros({1, flat_continuous_actions.size(-1)}, options); torch::Tensor continuous_target_entropy_sum = - torch::zeros({flat_continuous_actions.size(-1)}, options); - torch::Tensor discrete_entropy_sum = torch::zeros({1}, options); + torch::zeros({1, flat_continuous_actions.size(-1)}, options); + torch::Tensor discrete_entropy_sum = torch::zeros({1, 1}, options); int nb_actor_minibatches = 0; @@ -212,9 +212,10 @@ namespace arenai::agent { const auto continuous_target_entropy = truncated_normal_entropy(mu, target_sigma); // mean over batch - const auto mean_continuous_entropy = continuous_entropy.mean(0).detach(); - const auto mean_continuous_target_entropy = continuous_target_entropy.mean(0).detach(); - const auto mean_discrete_entropy = discrete_entropy.mean(0).detach(); + const auto mean_continuous_entropy = continuous_entropy.mean(0, true).detach(); + const auto mean_continuous_target_entropy = + continuous_target_entropy.mean(0, true).detach(); + const auto mean_discrete_entropy = discrete_entropy.mean(0, true).detach(); continuous_entropy_metric->add(mean_continuous_entropy.mean().item()); discrete_entropy_metric->add(mean_discrete_entropy.mean().item()); @@ -288,7 +289,7 @@ namespace arenai::agent { // train continuous alpha const auto continuous_alpha_loss = torch::sum( - alpha_continuous->log_alpha().squeeze(0) + alpha_continuous->log_alpha() * torch::detach(continuous_entropy - continuous_target_entropy)); alpha_continuous_optim->zero_grad(); @@ -296,8 +297,8 @@ namespace arenai::agent { alpha_continuous_optim->step(); // train discrete alpha - const auto discrete_alpha_loss = torch::mean( - alpha_discrete->log_alpha().squeeze(0) + const auto discrete_alpha_loss = torch::sum( + alpha_discrete->log_alpha() * torch::detach(discrete_entropy - target_discrete_entropy)); alpha_discrete_optim->zero_grad(); From 3ca9ffded98cfd34610608e54651ff168327b63e Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 17 Aug 2026 16:32:19 +0200 Subject: [PATCH 46/70] * re-add fire cost * re-add entropy bonus for PPO * change fire proba target from 0.05 to 0.3 --- arenai_agent/src/agents/ppo/ppo_factory.cpp | 14 +- .../src/agents/ppo/ppo_hyperparams.cpp | 4 +- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 4 +- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 196 +++--------------- arenai_agent/src/agents/ppo/ppo_trainer.h | 58 ++---- arenai_agent/src/networks/constants.h | 2 +- .../tests/src/tests_agents/tests_ppo.cpp | 9 +- .../src/tests_agents/tests_ppo_training.cpp | 8 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 25 ++- arenai_model/src/tank/jolt_enemy_tank.h | 10 + .../tests/src/tests_reward/tests_reward.cpp | 11 +- 11 files changed, 93 insertions(+), 248 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_factory.cpp b/arenai_agent/src/agents/ppo/ppo_factory.cpp index 74be5d18..9a1c23ab 100644 --- a/arenai_agent/src/agents/ppo/ppo_factory.cpp +++ b/arenai_agent/src/agents/ppo/ppo_factory.cpp @@ -21,13 +21,13 @@ namespace arenai::agent { collector(std::make_shared(rollout_buffer)), agent(std::make_shared(actor, device, collector)), trainer(std::make_shared( - actor, rollout_buffer, vision_height, vision_width, nb_sensors, nb_continuous_actions, - params.actor_learning_rate, params.critic_learning_rate, - params.continuous_alpha_learning_rate, params.discrete_alpha_learning_rate, - params.hidden_size_sensors, params.critic_hidden_sizes, params.vision_channels, - params.group_norm_nums, device, params.metric_window_size, params.gamma, - params.gae_lambda, params.clip_epsilon, params.target_kl, params.grad_norm_max, - params.epochs, params.rollout_size, params.minibatch_size)) {} + actor, rollout_buffer, vision_height, vision_width, nb_sensors, + params.actor_learning_rate, params.critic_learning_rate, params.hidden_size_sensors, + params.critic_hidden_sizes, params.vision_channels, params.group_norm_nums, device, + params.metric_window_size, params.gamma, params.gae_lambda, params.clip_epsilon, + params.target_kl, params.grad_norm_max, params.continuous_entropy_coefficient, + params.discrete_entropy_coefficient, params.epochs, params.rollout_size, + params.minibatch_size)) {} std::shared_ptr PpoTorchAgentFactory::get_agent() { return agent; } diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp index 713f1817..c2a91dc6 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp @@ -21,8 +21,8 @@ namespace arenai::agent { {"--clip_epsilon", &PpoHyperParams::clip_epsilon}, {"--target_kl", &PpoHyperParams::target_kl}, {"--grad_norm_max", &PpoHyperParams::grad_norm_max}, - {"--continuous_alpha_learning_rate", &PpoHyperParams::continuous_alpha_learning_rate}, - {"--discrete_alpha_learning_rate", &PpoHyperParams::discrete_alpha_learning_rate}, + {"--continuous_entropy_coefficient", &PpoHyperParams::continuous_entropy_coefficient}, + {"--discrete_entropy_coefficient", &PpoHyperParams::discrete_entropy_coefficient}, {"--epochs", &PpoHyperParams::epochs}, {"--rollout_size", &PpoHyperParams::rollout_size}, {"--minibatch_size", &PpoHyperParams::minibatch_size}, diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 883b7120..8bba41f9 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -32,8 +32,8 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - float continuous_alpha_learning_rate = 4e-2f; - float discrete_alpha_learning_rate = 1e-1f; + float continuous_entropy_coefficient = 2e-3f; + float discrete_entropy_coefficient = 2e-3f; int epochs = 4; int rollout_size = 30 * 30; int minibatch_size = 1024; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index d9178fcc..ce3bc941 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -8,7 +8,6 @@ #include "../../distributions/multinomial.h" #include "../../distributions/truncated_normal.h" -#include "../../metrics/last_metric.h" #include "../../metrics/mean_metric.h" #include "../../metrics/std_metric.h" #include "../../networks/constants.h" @@ -33,57 +32,36 @@ namespace arenai::agent { PpoTrainer::PpoTrainer( std::shared_ptr actor, std::shared_ptr rollout_buffer, const int vision_height, const int vision_width, const int nb_sensors, - const int nb_continuous_actions, const float actor_learning_rate, - const float critic_learning_rate, const float continuous_alpha_learning_rate, - const float discrete_alpha_learning_rate, const int hidden_size_sensors, - const std::vector &critic_hidden_sizes, + const float actor_learning_rate, const float critic_learning_rate, + const int hidden_size_sensors, const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, const torch::Device device, const int metric_window_size, const float gamma, const float gae_lambda, const float clip_epsilon, const float target_kl, const float grad_norm_max, + const float continuous_entropy_coefficient, const float discrete_entropy_coefficient, const int epochs, const int rollout_size, const int minibatch_size) : actor(std::move(actor)), rollout_buffer(std::move(rollout_buffer)), critic(std::make_shared( vision_height, vision_width, nb_sensors, hidden_size_sensors, critic_hidden_sizes, vision_channels, group_norm_nums)), - // the bounds are anti-windup guards, two decades on each side of the initial value: - // they stay inactive as long as the dual ascent behaves - alpha_continuous( - std::make_shared(1e-3f, 1e-5f, 1e-2f, nb_continuous_actions)), - alpha_discrete(std::make_shared(1e-3f, 1e-5f, 1e-2f, 1)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_optim(std::make_unique( critic->parameters(), torch::optim::AdamOptions(critic_learning_rate))), - alpha_continuous_optim(std::make_unique( - alpha_continuous->parameters(), - torch::optim::SGDOptions(continuous_alpha_learning_rate))), - alpha_discrete_optim(std::make_unique( - alpha_discrete->parameters(), - torch::optim::SGDOptions(discrete_alpha_learning_rate))), actor_mean_loss_metric(std::make_shared("π_μ", metric_window_size)), actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), critic_std_loss_metric(std::make_shared("v_σ", metric_window_size)), continuous_entropy_metric(std::make_shared("Hc", metric_window_size)), discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), - alpha_continuous_metric(std::make_shared("α_c", metric_window_size, 2, true)), - alpha_continuous_max_metric( - std::make_shared("α_c_max", metric_window_size, 2, true)), - alpha_discrete_metric(std::make_shared("α_d", metric_window_size, 2, true)), - continuous_target_entropy_metric(std::make_shared("Hc_t")), - discrete_target_entropy_metric(std::make_shared("Hd_t")), sigma_metric(std::make_shared("σ", metric_window_size)), - sigma_min_metric(std::make_shared("σ_min", metric_window_size)), clip_fraction_metric(std::make_shared("clip", metric_window_size)), kl_metric(std::make_shared("kl", metric_window_size, 2, true)), gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), - grad_norm_max(grad_norm_max), epochs(epochs), rollout_size(rollout_size), - minibatch_size(minibatch_size), - target_sigma( - torch::tensor(std::vector(nb_continuous_actions, TARGET_SIGMA)).unsqueeze(0)), - target_discrete_entropy( - torch::tensor({multinomial_target_entropy(TARGET_FIRE_PROBABILITY)}).unsqueeze(0)) { + grad_norm_max(grad_norm_max), + continuous_entropy_coefficient(continuous_entropy_coefficient), + discrete_entropy_coefficient(discrete_entropy_coefficient), epochs(epochs), + rollout_size(rollout_size), minibatch_size(minibatch_size) { to(device); } @@ -123,17 +101,6 @@ namespace arenai::agent { return tensor.index_select(0, idx).to(device); }; - // one running sum per continuous action; left undefined until the first minibatch so - // the action count does not have to be carried around - const auto options = torch::TensorOptions().device(device); - torch::Tensor continuous_entropy_sum = - torch::zeros({1, flat_continuous_actions.size(-1)}, options); - torch::Tensor continuous_target_entropy_sum = - torch::zeros({1, flat_continuous_actions.size(-1)}, options); - torch::Tensor discrete_entropy_sum = torch::zeros({1, 1}, options); - - int nb_actor_minibatches = 0; - bool kl_stop = false; for (int e = 0; e < epochs; e++) { const auto perm = valid_idx.index_select(0, torch::randperm(nb_valid_rows)); @@ -148,45 +115,20 @@ namespace arenai::agent { // actor: skipped for the rest of the update once a minibatch drifted past // the KL threshold - if (!kl_stop) { - const auto - [kl_exceeded, continuous_entropy, continuous_target_entropy, - discrete_entropy] = - train_actor( - mb_vision, mb_proprioception, select(flat_continuous_actions, idx), - select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), - select(flat_advantages, idx)); - - continuous_entropy_sum += continuous_entropy; - continuous_target_entropy_sum += continuous_target_entropy; - discrete_entropy_sum += discrete_entropy; - - nb_actor_minibatches++; - - kl_stop = kl_exceeded; - } + if (!kl_stop) + kl_stop = train_actor( + mb_vision, mb_proprioception, select(flat_continuous_actions, idx), + select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), + select(flat_advantages, idx)); // critic: keeps training through the whole update, the KL early stop is a // policy criterion only train_critic(mb_vision, mb_proprioception, select(flat_returns, idx)); } } - - // dual ascent, once per rollout on the mean entropy of the whole update: outside the - // epoch loop, an early KL stop can no longer freeze the entropy regulation - if (nb_actor_minibatches > 0) { - const auto nb_minibatches = static_cast(nb_actor_minibatches); - - const auto continuous_entropy = continuous_entropy_sum / nb_minibatches; - const auto continuous_target_entropy = continuous_target_entropy_sum / nb_minibatches; - - const auto discrete_entropy = discrete_entropy_sum / nb_minibatches; - - train_alphas(continuous_entropy, continuous_target_entropy, discrete_entropy); - } } - ActorUpdateResult PpoTrainer::train_actor( + bool PpoTrainer::train_actor( const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions, const torch::Tensor &old_log_probs, const torch::Tensor &advantages) const { @@ -204,23 +146,16 @@ namespace arenai::agent { const auto ratio = torch::exp(curr_continuous_log_probs + curr_discrete_log_probs - old_log_probs); - // kept per action: each dimension is regulated by its own coefficient, summing here - // would let a wide dimension hide a collapsed one const auto continuous_entropy = truncated_normal_entropy(mu, sigma); - const auto discrete_entropy = multinomial_entropy(discrete_proba); - const auto continuous_target_entropy = truncated_normal_entropy(mu, target_sigma); + // as in SAC: summed over the dimensions in the loss, averaged in the metric + continuous_entropy_metric->add(continuous_entropy.mean().item()); + sigma_metric->add(sigma.mean().item()); - // mean over batch - const auto mean_continuous_entropy = continuous_entropy.mean(0, true).detach(); - const auto mean_continuous_target_entropy = - continuous_target_entropy.mean(0, true).detach(); - const auto mean_discrete_entropy = discrete_entropy.mean(0, true).detach(); + // already summed over the discrete actions: multinomial_entropy keeps the last dim + const auto discrete_entropy = multinomial_entropy(discrete_proba); - continuous_entropy_metric->add(mean_continuous_entropy.mean().item()); - discrete_entropy_metric->add(mean_discrete_entropy.mean().item()); - sigma_metric->add(sigma.mean().item()); - sigma_min_metric->add(sigma.mean(0).min().item()); + discrete_entropy_metric->add(discrete_entropy.mean().item()); // approx KL (Schulman): E[(ratio - 1) - log ratio]; skip this minibatch when the // policy drifted too far from the rollout policy @@ -228,24 +163,17 @@ namespace arenai::agent { kl_metric->add(approx_kl); - if (target_kl > 0.f && approx_kl > 1.5f * target_kl) - return { - .kl_exceeded = true, - .continuous_entropy = mean_continuous_entropy, - .continuous_target_entropy = mean_continuous_target_entropy, - .discrete_entropy = mean_discrete_entropy}; + if (target_kl > 0.f && approx_kl > 1.5f * target_kl) return true; const auto clipped_ratio = torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); const auto surrogate = torch::min(ratio * advantages, clipped_ratio * advantages); - // one coefficient per action dimension, broadcast over the batch and summed back - const auto continuous_entropy_bonus = - torch::sum(alpha_continuous->alpha().detach() * continuous_entropy, -1, true); - + // the bonuses are the only upward force on the entropies: the surrogate shrinks both + // on its own, and neither head has a restoring force of its own const auto actor_loss = -torch::mean( - surrogate + continuous_entropy_bonus - + alpha_discrete->alpha().detach() * discrete_entropy); + surrogate + continuous_entropy_coefficient * torch::sum(continuous_entropy, -1, true) + + discrete_entropy_coefficient * discrete_entropy); actor_optim->zero_grad(); actor_loss.backward(); @@ -259,11 +187,7 @@ namespace arenai::agent { clip_fraction_metric->add( ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); - return { - .kl_exceeded = false, - .continuous_entropy = mean_continuous_entropy, - .continuous_target_entropy = mean_continuous_target_entropy, - .discrete_entropy = mean_discrete_entropy}; + return false; } void PpoTrainer::train_critic( @@ -283,38 +207,6 @@ namespace arenai::agent { critic_std_loss_metric->add(loss_value); } - void PpoTrainer::train_alphas( - const torch::Tensor &continuous_entropy, const torch::Tensor &continuous_target_entropy, - const torch::Tensor &discrete_entropy) const { - - // train continuous alpha - const auto continuous_alpha_loss = torch::sum( - alpha_continuous->log_alpha() - * torch::detach(continuous_entropy - continuous_target_entropy)); - - alpha_continuous_optim->zero_grad(); - continuous_alpha_loss.backward(); - alpha_continuous_optim->step(); - - // train discrete alpha - const auto discrete_alpha_loss = torch::sum( - alpha_discrete->log_alpha() - * torch::detach(discrete_entropy - target_discrete_entropy)); - - alpha_discrete_optim->zero_grad(); - discrete_alpha_loss.backward(); - alpha_discrete_optim->step(); - - // metrics - const auto continuous_alphas = alpha_continuous->alpha(); - alpha_continuous_metric->add(continuous_alphas.mean().item()); - alpha_continuous_max_metric->add(continuous_alphas.max().item()); - alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); - - continuous_target_entropy_metric->add(continuous_target_entropy.mean().item()); - discrete_target_entropy_metric->add(target_discrete_entropy.item()); - } - GaeResult PpoTrainer::compute_gae(const PpoRollout &rollout, const torch::Device device) const { torch::NoGradGuard no_grad; @@ -372,22 +264,9 @@ namespace arenai::agent { } std::vector> PpoTrainer::get_metrics() { - return { - actor_mean_loss_metric, - actor_std_loss_metric, - critic_mean_loss_metric, - critic_std_loss_metric, - continuous_target_entropy_metric, - continuous_entropy_metric, - alpha_continuous_metric, - alpha_continuous_max_metric, - discrete_target_entropy_metric, - discrete_entropy_metric, - alpha_discrete_metric, - sigma_metric, - sigma_min_metric, - clip_fraction_metric, - kl_metric}; + return {actor_mean_loss_metric, actor_std_loss_metric, critic_mean_loss_metric, + critic_std_loss_metric, continuous_entropy_metric, discrete_entropy_metric, + sigma_metric, clip_fraction_metric, kl_metric}; } void PpoTrainer::save(const std::filesystem::path &output_folder) { @@ -395,16 +274,10 @@ namespace arenai::agent { save_torch(output_folder, actor, "actor.pt"); save_torch(output_folder, critic, "critic.pt"); - save_torch(output_folder, alpha_continuous, "alpha_continuous.pt"); - save_torch(output_folder, alpha_discrete, "alpha_discrete.pt"); - // Optimizers save_torch(output_folder, actor_optim, "actor_optim.pt"); save_torch(output_folder, critic_optim, "critic_optim.pt"); - save_torch(output_folder, alpha_continuous_optim, "alpha_continuous_optim.pt"); - save_torch(output_folder, alpha_discrete_optim, "alpha_discrete_optim.pt"); - // string repr std::ostringstream actor_repr_oss; dump_module_tree(actor, actor_repr_oss, 0, "actor"); @@ -422,27 +295,16 @@ namespace arenai::agent { void PpoTrainer::set_train(const bool train) const { actor->train(train); critic->train(train); - - alpha_continuous->train(train); - alpha_discrete->train(train); } void PpoTrainer::to(const torch::Device device) { actor->to(device); critic->to(device); - - alpha_continuous->to(device); - alpha_discrete->to(device); - - target_sigma = target_sigma.to(device); - target_discrete_entropy = target_discrete_entropy.to(device); } int PpoTrainer::count_parameters() { return count_parameters_impl(actor->parameters()) - + count_parameters_impl(critic->parameters()) - + count_parameters_impl(alpha_continuous->parameters()) - + count_parameters_impl(alpha_discrete->parameters()); + + count_parameters_impl(critic->parameters()); } }// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 5a91342d..ba39b39b 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -6,7 +6,6 @@ #define ARENAI_PPO_TRAINER_H #include "../../networks/actor.h" -#include "../../networks/entropy.h" #include "../../networks/value_function.h" #include "../trainer.h" #include "./ppo_rollout_buffer.h" @@ -18,27 +17,18 @@ namespace arenai::agent { torch::Tensor returns; }; - struct ActorUpdateResult { - // the minibatch drifted past the KL threshold: no update was applied - bool kl_exceeded; - // one entropy per continuous action, detached: each dimension feeds its own alpha - torch::Tensor continuous_entropy; - torch::Tensor continuous_target_entropy; - torch::Tensor discrete_entropy; - }; - class PpoTrainer final : public AbstractTrainer { public: PpoTrainer( std::shared_ptr actor, std::shared_ptr rollout_buffer, - int vision_height, int vision_width, int nb_sensors, int nb_continuous_actions, - float actor_learning_rate, float critic_learning_rate, - float continuous_alpha_learning_rate, float discrete_alpha_learning_rate, - int hidden_size_sensors, const std::vector &critic_hidden_sizes, + int vision_height, int vision_width, int nb_sensors, float actor_learning_rate, + float critic_learning_rate, int hidden_size_sensors, + const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, torch::Device device, int metric_window_size, float gamma, float gae_lambda, float clip_epsilon, float target_kl, float grad_norm_max, - int epochs, int rollout_size, int minibatch_size); + float continuous_entropy_coefficient, float discrete_entropy_coefficient, int epochs, + int rollout_size, int minibatch_size); void step() override; @@ -54,14 +44,8 @@ namespace arenai::agent { std::shared_ptr critic; - // adaptive entropy coefficients (dual ascent toward fixed entropy targets) - std::shared_ptr alpha_continuous; - std::shared_ptr alpha_discrete; - std::shared_ptr actor_optim; std::shared_ptr critic_optim; - std::shared_ptr alpha_continuous_optim; - std::shared_ptr alpha_discrete_optim; std::shared_ptr actor_mean_loss_metric; std::shared_ptr actor_std_loss_metric; @@ -69,26 +53,13 @@ namespace arenai::agent { std::shared_ptr critic_mean_loss_metric; std::shared_ptr critic_std_loss_metric; + // both regulated by their constant entropy bonus std::shared_ptr continuous_entropy_metric; std::shared_ptr discrete_entropy_metric; - std::shared_ptr alpha_continuous_metric; - // alpha of the most constrained dimension: it is the one that rises when a single - // action collapses, which the mean hides - std::shared_ptr alpha_continuous_max_metric; - std::shared_ptr alpha_discrete_metric; - - std::shared_ptr continuous_target_entropy_metric; - std::shared_ptr discrete_target_entropy_metric; - // mean sigma of the truncated normal: direct view of the aim spread, Hc only bounds it std::shared_ptr sigma_metric; - // sigma of the tightest action dimension: the entropy target constrains the sum over - // the dimensions, so a single wide one can hide several collapsed ones behind a healthy - // Hc — and a collapsed dimension is what blows the log ratio up - std::shared_ptr sigma_min_metric; - std::shared_ptr clip_fraction_metric; std::shared_ptr kl_metric; @@ -100,21 +71,20 @@ namespace arenai::agent { float grad_norm_max; + float continuous_entropy_coefficient; + float discrete_entropy_coefficient; + int epochs; // rollout horizon: one train() consumes rollout_size complete steps in a single update int rollout_size; // rows per gradient step; also the chunk size for the no-grad value evaluation int minibatch_size; - torch::Tensor target_sigma; - torch::Tensor target_discrete_entropy; - void train() const; - // one backward pass on the actor for a single minibatch; the update is skipped - // when the minibatch already drifted past the KL threshold, its entropies are - // reported either way so the caller can feed the dual ascent - ActorUpdateResult train_actor( + // one backward pass on the actor for a single minibatch; returns true when the + // minibatch already drifted past the KL threshold, in which case no update was applied + bool train_actor( const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions, const torch::Tensor &old_log_probs, const torch::Tensor &advantages) const; @@ -124,10 +94,6 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &returns) const; - void train_alphas( - const torch::Tensor &continuous_entropy, const torch::Tensor &continuous_target_entropy, - const torch::Tensor &discrete_entropy) const; - // GAE advantages (normalized over the valid pairs) and value targets, // computed with the pre-update critic GaeResult compute_gae(const PpoRollout &rollout, torch::Device device) const; diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index b70322f5..a1a5fe9a 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -9,7 +9,7 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; constexpr float TARGET_SIGMA = 0.1f; - constexpr float TARGET_FIRE_PROBABILITY = 0.05f; + constexpr float TARGET_FIRE_PROBABILITY = 0.3f; constexpr float SIGMA_MIN = 1e-4f; constexpr float SIGMA_MAX = 2.f; diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index d882cca3..555b2123 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -32,8 +32,6 @@ std::unique_ptr PpoAgentTest::make_factory(const PpoTestCo .gae_lambda = 0.95f, .clip_epsilon = 0.2f, .grad_norm_max = 1.f, - .continuous_alpha_learning_rate = 1e-3f, - .discrete_alpha_learning_rate = 1e-3f, .epochs = 1, .rollout_size = 8, .minibatch_size = 10}; @@ -77,7 +75,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 15); + ASSERT_EQ(metrics.size(), 9); } // ======================================================================== @@ -146,9 +144,8 @@ TEST_P(PpoSaveLoadParamTest, SaveCreatesExpectedFiles) { factory->get_trainer()->save(save_dir); const std::vector expected_files = { - "actor.pt", "critic.pt", "alpha_continuous.pt", "alpha_discrete.pt", - "actor_optim.pt", "critic_optim.pt", "alpha_continuous_optim.pt", "alpha_discrete_optim.pt", - "actor_repr.txt", "critic_repr.txt", + "actor.pt", "critic.pt", "actor_optim.pt", + "critic_optim.pt", "actor_repr.txt", "critic_repr.txt", }; for (const auto &f: expected_files) diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index d0c22a70..0833c81e 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -21,8 +21,6 @@ PpoTrainingTest::make_factory(const PpoTrainingTestConfig &cfg) const { .gamma = 0.99f, .gae_lambda = 0.95f, .clip_epsilon = 0.2f, - .continuous_alpha_learning_rate = 1e-3f, - .discrete_alpha_learning_rate = 1e-3f, .epochs = 2, .rollout_size = ROLLOUT_SIZE, .minibatch_size = MINIBATCH_SIZE}; @@ -76,9 +74,9 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { const auto agent = std::make_shared(actor, device, collector); // target_kl = 0 : early stop disabled so every minibatch applies its update const auto trainer = std::make_shared( - actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, - cfg.nb_continuous_actions, 1e-3f, 1e-3f, 1e-3f, 1e-3f, 8, std::vector{16}, vision_channels, - group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, 1.f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE); + actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, 1e-3f, 1e-3f, 8, + std::vector{16}, vision_channels, group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, 1.f, + 2e-3f, 2e-3f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE); std::vector initial_parameters; for (const auto ¶meter: actor->parameters()) diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 3d752345..86c2eb15 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -57,17 +57,19 @@ namespace arenai::model { curr_frame_upside_down(0), miss_distance_scale(10.f), hit_reward_scale(0.5f), // 10 shells at 6 shots/s: the reserve binds after ~1.5 s of spam instead of 5 s, // so running dry still falls inside the discount horizon of the shots that - // caused it — a scarcity the policy can see (proprioception) rather than a - // per-shot penalty, which would make firing negative before aiming is learned - hit_received_cost(0.1f), initial_nb_shells(10), nb_shells(initial_nb_shells), - shells_recharged_per_hit(5), + // caused it. The scarcity alone was not enough — train_344 fired at the full + // budget (1.5 shell/s) with a 2.3% hit rate, because a randomly aimed shell still + // earns 0.115 in a 32-tank arena. fire_cost puts the average shell slightly in the + // red, so the trigger has to become selective instead of merely rate-limited + hit_received_cost(0.1f), fire_cost(0.2f), initial_nb_shells(10), + nb_shells(initial_nb_shells), shells_recharged_per_hit(5), // the reserve also refills on its own, one shell every 1.5 s: with the recharge // conditioned on a hit alone, a policy that never hits stays dry for the rest of // its life — an absorbing state where the fire action is a no-op and stops // producing any gradient. The hit recharge remains the fast path nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), - curr_frame_shell_regen(0), is_dead_already_triggered(false), has_touch(false), - has_fired(false) {} + curr_frame_shell_regen(0), nb_shells_fired_since_reward(0), + is_dead_already_triggered(false), has_touch(false), has_fired(false) {} float JoltEnemyTank::compute_hit_reward( const glm::vec3 &fire_pos, const glm::vec3 &enemy_pos, const glm::vec3 &shell_pos) const { @@ -183,12 +185,16 @@ namespace arenai::model { tracked_shells.erase(tracked_shells.begin() + i); } - // 5. hits received penalty + // 5. fired shells cost, paid up front and independently of the outcome + const float fire_penalty = -fire_cost * static_cast(nb_shells_fired_since_reward); + nb_shells_fired_since_reward = 0; + + // 6. hits received penalty const float hit_received_penalty = -hit_received_cost * static_cast(get_received_hits()); - // 6. total reward - const float reward = dead_penalty + shells_reward + hit_received_penalty; + // 7. total reward + const float reward = dead_penalty + shells_reward + fire_penalty + hit_received_penalty; return reward; } @@ -196,6 +202,7 @@ namespace arenai::model { void JoltEnemyTank::on_shell_fired(const std::shared_ptr &shell) { nb_shells--; has_fired = true; + nb_shells_fired_since_reward++; tracked_shells.push_back( {.shell = shell, diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index 7674f0bf..f43515fd 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -71,12 +71,22 @@ namespace arenai::model { float hit_received_cost; + // paid per shell actually fired, not per requested trigger: the request is blocked by + // the cooldown and the reserve, charging it would bill noise the policy cannot control. + // Calibrated above the 0.115 a randomly aimed shell earns in a 32-tank arena, so that + // spraying stops being profitable and the trigger has to become selective + float fire_cost; + int initial_nb_shells; int nb_shells; int shells_recharged_per_hit; int nb_frames_per_shell_regen; int curr_frame_shell_regen; + // shells fired since the last get_reward: has_fired is consumed by has_fired_shell() + // for the metric, so the reward keeps its own counter + int nb_shells_fired_since_reward; + bool is_dead_already_triggered; bool has_touch; diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index e3f5430d..08427063 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -14,6 +14,9 @@ using namespace arenai::model; using namespace arenai::utils; using namespace arenai::controller; +// mirrors JoltEnemyTank::fire_cost: a fired shell is charged up front, whatever it hits +constexpr float FIRE_COST = 0.2f; + // ======================================================================== // get_reward — base scenarios // ======================================================================== @@ -220,7 +223,9 @@ TEST_F(RewardTest, NoRewardWhenShootingAWreck) { const float reward = shared_a->get_reward(tanks); ASSERT_FALSE(shared_a->has_hit_other_tank()) << "a wreck must not count as a hit"; - ASSERT_FLOAT_EQ(reward, 0.f) << "shooting a wreck must pay neither hit nor kill"; + // the shell is still charged: only the hit/kill/aim payout is withheld, so a wreck is a + // strictly losing target rather than a neutral one + ASSERT_FLOAT_EQ(reward, -FIRE_COST) << "shooting a wreck must pay neither hit nor kill"; // the shell spent must not be given back: a wreck is not an ammo dump ASSERT_LT(shared_a->get_proprioception().back(), shells_ratio_before) @@ -254,6 +259,6 @@ TEST_F(RewardTest, ZeroRewardWithEmptyTankList) { ASSERT_FALSE(std::isnan(reward)) << "reward should not be NaN with empty tank list"; ASSERT_FALSE(std::isinf(reward)) << "reward should not be Inf with empty tank list"; - // firing is free and there is no enemy to sample, so no reward at all - ASSERT_FLOAT_EQ(reward, 0.f); + // there is no enemy to sample, so the shell pays nothing back and only its cost remains + ASSERT_FLOAT_EQ(reward, -FIRE_COST); } From 6dc2faa0835603a5bf3dbab8fb869e03744e7d63 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 17 Aug 2026 17:56:30 +0200 Subject: [PATCH 47/70] update reward : ratio = 1.5 * miss / sqrt(distance) --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 2 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 34 ++++++------------- arenai_model/src/tank/jolt_enemy_tank.h | 10 ------ .../tests/src/tests_reward/tests_reward.cpp | 11 ++---- 4 files changed, 14 insertions(+), 43 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 8bba41f9..685cb647 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -33,7 +33,7 @@ namespace arenai::agent { float target_kl = 0.02f; float grad_norm_max = 0.5f; float continuous_entropy_coefficient = 2e-3f; - float discrete_entropy_coefficient = 2e-3f; + float discrete_entropy_coefficient = 1e-2f; int epochs = 4; int rollout_size = 30 * 30; int minibatch_size = 1024; diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 86c2eb15..d6e010a2 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -54,22 +54,12 @@ namespace arenai::model { [this](const std::shared_ptr &shell) { on_shell_fired(shell); }, [this] { return nb_shells > 0; }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), - curr_frame_upside_down(0), miss_distance_scale(10.f), hit_reward_scale(0.5f), - // 10 shells at 6 shots/s: the reserve binds after ~1.5 s of spam instead of 5 s, - // so running dry still falls inside the discount horizon of the shots that - // caused it. The scarcity alone was not enough — train_344 fired at the full - // budget (1.5 shell/s) with a 2.3% hit rate, because a randomly aimed shell still - // earns 0.115 in a 32-tank arena. fire_cost puts the average shell slightly in the - // red, so the trigger has to become selective instead of merely rate-limited - hit_received_cost(0.1f), fire_cost(0.2f), initial_nb_shells(10), - nb_shells(initial_nb_shells), shells_recharged_per_hit(5), - // the reserve also refills on its own, one shell every 1.5 s: with the recharge - // conditioned on a hit alone, a policy that never hits stays dry for the rest of - // its life — an absorbing state where the fire action is a no-op and stops - // producing any gradient. The hit recharge remains the fast path + curr_frame_upside_down(0), miss_distance_scale(1.5f), hit_reward_scale(0.5f), + hit_received_cost(0.1f), initial_nb_shells(10), nb_shells(initial_nb_shells), + shells_recharged_per_hit(5), nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), - curr_frame_shell_regen(0), nb_shells_fired_since_reward(0), - is_dead_already_triggered(false), has_touch(false), has_fired(false) {} + curr_frame_shell_regen(0), is_dead_already_triggered(false), has_touch(false), + has_fired(false) {} float JoltEnemyTank::compute_hit_reward( const glm::vec3 &fire_pos, const glm::vec3 &enemy_pos, const glm::vec3 &shell_pos) const { @@ -80,7 +70,8 @@ namespace arenai::model { const float ideal_trajectory_distance = glm::length(ideal_trajectory); const float miss_distance = glm::length(miss_trajectory); - const float ratio = miss_distance_scale * miss_distance / ideal_trajectory_distance; + const float ratio = + miss_distance_scale * miss_distance / std::sqrt(ideal_trajectory_distance); return std::exp(-0.5f * std::pow(ratio, 2.f)); } @@ -185,16 +176,12 @@ namespace arenai::model { tracked_shells.erase(tracked_shells.begin() + i); } - // 5. fired shells cost, paid up front and independently of the outcome - const float fire_penalty = -fire_cost * static_cast(nb_shells_fired_since_reward); - nb_shells_fired_since_reward = 0; - - // 6. hits received penalty + // 5. hits received penalty const float hit_received_penalty = -hit_received_cost * static_cast(get_received_hits()); - // 7. total reward - const float reward = dead_penalty + shells_reward + fire_penalty + hit_received_penalty; + // 6. total reward + const float reward = dead_penalty + shells_reward + hit_received_penalty; return reward; } @@ -202,7 +189,6 @@ namespace arenai::model { void JoltEnemyTank::on_shell_fired(const std::shared_ptr &shell) { nb_shells--; has_fired = true; - nb_shells_fired_since_reward++; tracked_shells.push_back( {.shell = shell, diff --git a/arenai_model/src/tank/jolt_enemy_tank.h b/arenai_model/src/tank/jolt_enemy_tank.h index f43515fd..7674f0bf 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.h +++ b/arenai_model/src/tank/jolt_enemy_tank.h @@ -71,22 +71,12 @@ namespace arenai::model { float hit_received_cost; - // paid per shell actually fired, not per requested trigger: the request is blocked by - // the cooldown and the reserve, charging it would bill noise the policy cannot control. - // Calibrated above the 0.115 a randomly aimed shell earns in a 32-tank arena, so that - // spraying stops being profitable and the trigger has to become selective - float fire_cost; - int initial_nb_shells; int nb_shells; int shells_recharged_per_hit; int nb_frames_per_shell_regen; int curr_frame_shell_regen; - // shells fired since the last get_reward: has_fired is consumed by has_fired_shell() - // for the metric, so the reward keeps its own counter - int nb_shells_fired_since_reward; - bool is_dead_already_triggered; bool has_touch; diff --git a/arenai_model/tests/src/tests_reward/tests_reward.cpp b/arenai_model/tests/src/tests_reward/tests_reward.cpp index 08427063..e3f5430d 100644 --- a/arenai_model/tests/src/tests_reward/tests_reward.cpp +++ b/arenai_model/tests/src/tests_reward/tests_reward.cpp @@ -14,9 +14,6 @@ using namespace arenai::model; using namespace arenai::utils; using namespace arenai::controller; -// mirrors JoltEnemyTank::fire_cost: a fired shell is charged up front, whatever it hits -constexpr float FIRE_COST = 0.2f; - // ======================================================================== // get_reward — base scenarios // ======================================================================== @@ -223,9 +220,7 @@ TEST_F(RewardTest, NoRewardWhenShootingAWreck) { const float reward = shared_a->get_reward(tanks); ASSERT_FALSE(shared_a->has_hit_other_tank()) << "a wreck must not count as a hit"; - // the shell is still charged: only the hit/kill/aim payout is withheld, so a wreck is a - // strictly losing target rather than a neutral one - ASSERT_FLOAT_EQ(reward, -FIRE_COST) << "shooting a wreck must pay neither hit nor kill"; + ASSERT_FLOAT_EQ(reward, 0.f) << "shooting a wreck must pay neither hit nor kill"; // the shell spent must not be given back: a wreck is not an ammo dump ASSERT_LT(shared_a->get_proprioception().back(), shells_ratio_before) @@ -259,6 +254,6 @@ TEST_F(RewardTest, ZeroRewardWithEmptyTankList) { ASSERT_FALSE(std::isnan(reward)) << "reward should not be NaN with empty tank list"; ASSERT_FALSE(std::isinf(reward)) << "reward should not be Inf with empty tank list"; - // there is no enemy to sample, so the shell pays nothing back and only its cost remains - ASSERT_FLOAT_EQ(reward, -FIRE_COST); + // firing is free and there is no enemy to sample, so no reward at all + ASSERT_FLOAT_EQ(reward, 0.f); } From 8ec924e7308fa3f9e0de9a3f0f4be8fdbd140fa1 Mon Sep 17 00:00:00 2001 From: samuel Date: Mon, 17 Aug 2026 19:14:47 +0200 Subject: [PATCH 48/70] no reward norm for SAC --- .../src/agents/sac/sac_replay_buffer.cpp | 34 ++----------------- .../src/agents/sac/sac_replay_buffer.h | 9 ----- .../tests_replay_buffer_edge.cpp | 18 ---------- 3 files changed, 3 insertions(+), 58 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp index 58c4a949..0a4b0942 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.cpp +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.cpp @@ -5,7 +5,6 @@ #include "./sac_replay_buffer.h" #include -#include using namespace arenai; using namespace arenai::agent; @@ -14,7 +13,7 @@ namespace arenai::agent { SacReplayBuffer::SacReplayBuffer(const int memory_size) : initialized_(false), memory_size_(memory_size), nb_steps_(0), write_idx_(0), size_(0), - nb_tanks_(0), reward_count_(0.), reward_mean_(0.), reward_m2_(0.) {} + nb_tanks_(0) {} void SacReplayBuffer::initialize(const SacInputStep &first_step) { constexpr auto cpu = torch::kCPU; @@ -62,9 +61,7 @@ namespace arenai::agent { store_done_[idx].copy_(done_bool); // tanks already terminated before this step have no valid transition to store - const auto valid_mask = already_terminated_.logical_not(); - store_sampleable_[idx].copy_(valid_mask); - update_reward_stats(step.reward, valid_mask); + store_sampleable_[idx].copy_(already_terminated_.logical_not()); already_terminated_.logical_or_(done_bool.reshape({nb_tanks_})); advance_write_idx(); @@ -90,31 +87,6 @@ namespace arenai::agent { advance_write_idx(); } - void SacReplayBuffer::update_reward_stats( - const torch::Tensor &rewards, const torch::Tensor &valid_mask) { - // Chan et al. parallel Welford update with the batch of valid tank rewards - const auto valid_rewards = - rewards.reshape({nb_tanks_}).to(torch::kFloat).masked_select(valid_mask); - const auto batch_count = static_cast(valid_rewards.numel()); - if (batch_count == 0.) return; - - const auto batch_mean = valid_rewards.mean().item(); - const auto batch_m2 = (valid_rewards - batch_mean).square().sum().item(); - - const double delta = batch_mean - reward_mean_; - const double total_count = reward_count_ + batch_count; - - reward_mean_ += delta * batch_count / total_count; - reward_m2_ += batch_m2 + delta * delta * reward_count_ * batch_count / total_count; - reward_count_ = total_count; - } - - float SacReplayBuffer::reward_scale() const { - if (reward_count_ < 2.) return 1.f; - const double std = std::sqrt(reward_m2_ / reward_count_); - return std > 1e-6 ? static_cast(std) : 1.f; - } - void SacReplayBuffer::advance_write_idx() { write_idx_ = (write_idx_ + 1) % nb_steps_; if (size_ < nb_steps_) size_++; @@ -150,7 +122,7 @@ namespace arenai::agent { .action = {.continuous_action = take(store_cont_action_, step_idx), .discrete_action = take(store_disc_action_, step_idx)}, - .reward = take(store_reward_, step_idx) / reward_scale(), + .reward = take(store_reward_, step_idx), .done = take(store_done_, step_idx), .next_state = { .vision = take(store_vision_, next_idx), diff --git a/arenai_agent/src/agents/sac/sac_replay_buffer.h b/arenai_agent/src/agents/sac/sac_replay_buffer.h index 48907149..9f86e96c 100644 --- a/arenai_agent/src/agents/sac/sac_replay_buffer.h +++ b/arenai_agent/src/agents/sac/sac_replay_buffer.h @@ -42,8 +42,6 @@ namespace arenai::agent { private: void initialize(const SacInputStep &first_step); void advance_write_idx(); - void update_reward_stats(const torch::Tensor &rewards, const torch::Tensor &valid_mask); - float reward_scale() const; bool initialized_; @@ -66,13 +64,6 @@ namespace arenai::agent { torch::Tensor store_sampleable_; // [nb_tanks] tanks already done in the current episode torch::Tensor already_terminated_; - - // running reward statistics (Welford, over every reward ever added): rewards are - // divided by the running std at sample time, never mean-shifted (a shift would - // change the optimal policy) - double reward_count_; - double reward_mean_; - double reward_m2_; }; }// namespace arenai::agent diff --git a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp index 93368a59..72df747d 100644 --- a/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp +++ b/arenai_agent/tests/src/tests_replay_buffer/tests_replay_buffer_edge.cpp @@ -76,24 +76,6 @@ TEST_F(ReplayBufferEdgeTest, ConstantRewardsAreNotRescaled) { << "Zero reward variance must keep the scale at 1 (no division by ~0)"; } -TEST_F(ReplayBufferEdgeTest, RewardDividedByRunningStdAtSample) { - SacReplayBuffer buffer(10); - - // rewards {0, 4}: mean 2, population std 2 - buffer.add(create_step_with_reward(0.0f)); - buffer.add(create_step_with_reward(4.0f)); - buffer.finish_episode(create_random_state(8, 8, 5)); - - const auto output = buffer.sample(64, torch::kCPU); - - const auto rewards = output.reward.reshape({-1}); - for (int64_t i = 0; i < rewards.size(0); i++) { - const auto r = rewards[i].item(); - ASSERT_TRUE(std::abs(r - 0.0f) < 1e-5f || std::abs(r - 2.0f) < 1e-5f) - << "Sampled reward " << r << " should be a stored reward divided by the running std"; - } -} - TEST_F(ReplayBufferEdgeTest, DeadStepIsStoredAsTerminal) { SacReplayBuffer buffer(10); From 01badec83289a090511dce5fa8d829827479991d Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 17 Aug 2026 21:26:47 +0200 Subject: [PATCH 49/70] PPO : continue training on other minibatch when kl exceed --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 40 +++++++++++++-------- arenai_agent/src/agents/ppo/ppo_trainer.h | 7 +++- 2 files changed, 31 insertions(+), 16 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index ce3bc941..351da090 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -56,8 +56,9 @@ namespace arenai::agent { discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), sigma_metric(std::make_shared("σ", metric_window_size)), clip_fraction_metric(std::make_shared("clip", metric_window_size)), - kl_metric(std::make_shared("kl", metric_window_size, 2, true)), gamma(gamma), - gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), + kl_metric(std::make_shared("kl", metric_window_size, 2, true)), + skip_fraction_metric(std::make_shared("skip", metric_window_size)), + gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), grad_norm_max(grad_norm_max), continuous_entropy_coefficient(continuous_entropy_coefficient), discrete_entropy_coefficient(discrete_entropy_coefficient), epochs(epochs), @@ -101,7 +102,6 @@ namespace arenai::agent { return tensor.index_select(0, idx).to(device); }; - bool kl_stop = false; for (int e = 0; e < epochs; e++) { const auto perm = valid_idx.index_select(0, torch::randperm(nb_valid_rows)); @@ -113,13 +113,14 @@ namespace arenai::agent { const auto mb_vision = select(flat_vision, idx); const auto mb_proprioception = select(flat_proprioception, idx); - // actor: skipped for the rest of the update once a minibatch drifted past - // the KL threshold - if (!kl_stop) - kl_stop = train_actor( - mb_vision, mb_proprioception, select(flat_continuous_actions, idx), - select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), - select(flat_advantages, idx)); + // actor: a minibatch past the KL threshold skips its own update only. Latching + // the stop for the rest of the update let a few outlier rows cost the 111 + // other minibatches of the rollout — the recurring cause of the post-peak + // decay of train_341/343/344/346 + train_actor( + mb_vision, mb_proprioception, select(flat_continuous_actions, idx), + select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), + select(flat_advantages, idx)); // critic: keeps training through the whole update, the KL early stop is a // policy criterion only @@ -163,7 +164,18 @@ namespace arenai::agent { kl_metric->add(approx_kl); - if (target_kl > 0.f && approx_kl > 1.5f * target_kl) return true; + // recorded before the skip, unlike the actor losses: clip then describes the same + // population of minibatches as kl, whatever the skip rate + clip_fraction_metric->add( + ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); + + const bool kl_exceeded = target_kl > 0.f && approx_kl > 1.5f * target_kl; + + // how much of the update the KL threshold is eating. It is also the only reading left + // when a single underflowed ratio turns the whole kl window into inf + skip_fraction_metric->add(kl_exceeded ? 1.f : 0.f); + + if (kl_exceeded) return true; const auto clipped_ratio = torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); @@ -184,9 +196,6 @@ namespace arenai::agent { actor_mean_loss_metric->add(loss_value); actor_std_loss_metric->add(loss_value); - clip_fraction_metric->add( - ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); - return false; } @@ -266,7 +275,8 @@ namespace arenai::agent { std::vector> PpoTrainer::get_metrics() { return {actor_mean_loss_metric, actor_std_loss_metric, critic_mean_loss_metric, critic_std_loss_metric, continuous_entropy_metric, discrete_entropy_metric, - sigma_metric, clip_fraction_metric, kl_metric}; + sigma_metric, clip_fraction_metric, kl_metric, + skip_fraction_metric}; } void PpoTrainer::save(const std::filesystem::path &output_folder) { diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index ba39b39b..b4e27109 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -60,9 +60,13 @@ namespace arenai::agent { // mean sigma of the truncated normal: direct view of the aim spread, Hc only bounds it std::shared_ptr sigma_metric; + // both recorded on every attempted minibatch, skipped ones included std::shared_ptr clip_fraction_metric; std::shared_ptr kl_metric; + // fraction of minibatches the KL threshold skipped + std::shared_ptr skip_fraction_metric; + float gamma; float gae_lambda; float clip_epsilon; @@ -83,7 +87,8 @@ namespace arenai::agent { void train() const; // one backward pass on the actor for a single minibatch; returns true when the - // minibatch already drifted past the KL threshold, in which case no update was applied + // minibatch drifted past the KL threshold, in which case no update was applied and + // only the kl metric was recorded — the next minibatch is tried either way bool train_actor( const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions, From 38acf005588ef4dd576909784c3c84916168a974 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 17 Aug 2026 21:27:44 +0200 Subject: [PATCH 50/70] PPO : continue training on other minibatch when kl exceed --- arenai_agent/tests/src/tests_agents/tests_ppo.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index 555b2123..341cb496 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -75,7 +75,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 9); + ASSERT_EQ(metrics.size(), 10); } // ======================================================================== From 1d71c95e8e006862c61e412e63b69c45370573be Mon Sep 17 00:00:00 2001 From: samuel Date: Mon, 17 Aug 2026 22:37:58 +0200 Subject: [PATCH 51/70] refactor ambiguity between params (initial/target sigma & fire proba) => CLI args --- arenai_agent/src/agents/factory_set.cpp | 3 ++- arenai_agent/src/agents/ppo/ppo_factory.cpp | 2 +- .../src/agents/ppo/ppo_hyperparams.cpp | 2 ++ arenai_agent/src/agents/ppo/ppo_hyperparams.h | 2 ++ arenai_agent/src/agents/sac/sac_factory.cpp | 4 ++-- .../src/agents/sac/sac_hyperparams.cpp | 2 ++ arenai_agent/src/agents/sac/sac_hyperparams.h | 2 ++ arenai_agent/src/agents/sac/sac_trainer.cpp | 7 ++++--- arenai_agent/src/agents/sac/sac_trainer.h | 3 ++- arenai_agent/src/networks/actor.cpp | 9 ++++++--- arenai_agent/src/networks/actor.h | 3 ++- arenai_agent/src/networks/constants.h | 3 --- arenai_agent/src/networks_utils/init.h | 5 ++--- .../src/tests_agents/tests_ppo_training.cpp | 2 +- .../tests/src/tests_agents/tests_sac.cpp | 5 +++-- .../tests/src/tests_networks/tests_actor.cpp | 2 +- .../tests_q_function_consistency.cpp | 4 ++-- .../src/tests_networks_utils/tests_init.cpp | 19 +++++++++++++++++-- 18 files changed, 53 insertions(+), 26 deletions(-) diff --git a/arenai_agent/src/agents/factory_set.cpp b/arenai_agent/src/agents/factory_set.cpp index 62b171c8..5922821e 100644 --- a/arenai_agent/src/agents/factory_set.cpp +++ b/arenai_agent/src/agents/factory_set.cpp @@ -31,7 +31,8 @@ namespace arenai::agent { .channels, get_value( "group_norm_nums", parse_cli_group_norms, {{{1, 2, 3, 4, 6, 8}}}) - .groups), + .groups, + 0.f, 0.f), get_value("cuda", false) ? torch::kCUDA : torch::kCPU); } diff --git a/arenai_agent/src/agents/ppo/ppo_factory.cpp b/arenai_agent/src/agents/ppo/ppo_factory.cpp index 9a1c23ab..fa7130fb 100644 --- a/arenai_agent/src/agents/ppo/ppo_factory.cpp +++ b/arenai_agent/src/agents/ppo/ppo_factory.cpp @@ -16,7 +16,7 @@ namespace arenai::agent { : actor(std::make_shared( vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, params.hidden_size_sensors, params.actor_hidden_sizes, params.vision_channels, - params.group_norm_nums)), + params.group_norm_nums, params.initial_sigma, params.initial_fire_proba)), rollout_buffer(std::make_shared()), collector(std::make_shared(rollout_buffer)), agent(std::make_shared(actor, device, collector)), diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp index c2a91dc6..8445cf8a 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp @@ -15,6 +15,8 @@ namespace arenai::agent { {"--critic_hidden_sizes", &PpoHyperParams::critic_hidden_sizes}, {"--vision_channels", &PpoHyperParams::vision_channels}, {"--group_norm_nums", &PpoHyperParams::group_norm_nums}, + {"--initial_sigma", &PpoHyperParams::initial_sigma}, + {"--initial_fire_proba", &PpoHyperParams::initial_fire_proba}, {"--metric_window_size", &PpoHyperParams::metric_window_size}, {"--gamma", &PpoHyperParams::gamma}, {"--gae_lambda", &PpoHyperParams::gae_lambda}, diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 685cb647..051499e2 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -22,6 +22,8 @@ namespace arenai::agent { std::vector> vision_channels = {{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}; std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; + float initial_sigma = 0.5f; + float initial_fire_proba = 0.3f; int metric_window_size = 256; // 0.997 at 30 Hz -> ~11 s credit horizon (shell flight time + fights stay visible) float gamma = 0.997f; diff --git a/arenai_agent/src/agents/sac/sac_factory.cpp b/arenai_agent/src/agents/sac/sac_factory.cpp index d5166313..96b4a091 100644 --- a/arenai_agent/src/agents/sac/sac_factory.cpp +++ b/arenai_agent/src/agents/sac/sac_factory.cpp @@ -16,7 +16,7 @@ namespace arenai::agent { : actor(std::make_shared( vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, params.hidden_size_sensors, params.actor_hidden_sizes, params.vision_channels, - params.group_norm_nums)), + params.group_norm_nums, params.target_sigma, params.target_fire_proba)), replay_buffer(std::make_shared(params.replay_buffer_size)), collector(std::make_shared(replay_buffer)), agent(std::make_shared(actor, device, collector)), @@ -26,7 +26,7 @@ namespace arenai::agent { params.alpha_learning_rate, params.hidden_size_sensors, params.hidden_size_actions, params.critic_hidden_sizes, params.vision_channels, params.group_norm_nums, device, params.metric_window_size, params.tau, params.gamma, params.train_every, - params.epochs, params.batch_size)) {} + params.epochs, params.batch_size, params.target_sigma, params.target_fire_proba)) {} std::shared_ptr SacTorchAgentFactory::get_agent() { return agent; } diff --git a/arenai_agent/src/agents/sac/sac_hyperparams.cpp b/arenai_agent/src/agents/sac/sac_hyperparams.cpp index 70657322..198161d1 100644 --- a/arenai_agent/src/agents/sac/sac_hyperparams.cpp +++ b/arenai_agent/src/agents/sac/sac_hyperparams.cpp @@ -17,6 +17,8 @@ namespace arenai::agent { {.name = "--critic_hidden_sizes", .member = &SacHyperParams::critic_hidden_sizes}, {.name = "--vision_channels", .member = &SacHyperParams::vision_channels}, {.name = "--group_norm_nums", .member = &SacHyperParams::group_norm_nums}, + {.name = "--target_sigma", .member = &SacHyperParams::target_sigma}, + {.name = "--target_fire_proba", .member = &SacHyperParams::target_fire_proba}, {.name = "--metric_window_size", .member = &SacHyperParams::metric_window_size}, {.name = "--tau", .member = &SacHyperParams::tau}, {.name = "--gamma", .member = &SacHyperParams::gamma}, diff --git a/arenai_agent/src/agents/sac/sac_hyperparams.h b/arenai_agent/src/agents/sac/sac_hyperparams.h index 63218028..b215b4a9 100644 --- a/arenai_agent/src/agents/sac/sac_hyperparams.h +++ b/arenai_agent/src/agents/sac/sac_hyperparams.h @@ -24,6 +24,8 @@ namespace arenai::agent { std::vector> vision_channels = {{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}; std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; + float target_sigma = 0.1f; + float target_fire_proba = 0.05f; int metric_window_size = 256; float tau = 0.005f; float gamma = 0.997f; diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index bc2133a6..9de12ec1 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -32,7 +32,8 @@ namespace arenai::agent { const std::vector> &vision_channels, const std::vector &group_norm_nums, const torch::Device device, const int metric_window_size, const float tau, const float gamma, const int train_every, - const int epochs, const int batch_size) + const int epochs, const int batch_size, const float target_sigma, + const float target_fire_proba) : actor(std::move(actor)), replay_buffer(std::move(replay_buffer)), critic_1(std::make_shared( vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, @@ -78,9 +79,9 @@ namespace arenai::agent { gamma(gamma), train_every(train_every), train_counter(0), epochs(epochs), batch_size(batch_size), target_sigma( - torch::tensor(std::vector(nb_continuous_actions, TARGET_SIGMA)).unsqueeze(0)), + torch::tensor(std::vector(nb_continuous_actions, target_sigma)).unsqueeze(0)), target_discrete_entropy( - torch::tensor({multinomial_target_entropy(TARGET_FIRE_PROBABILITY)}).unsqueeze(0)) { + torch::tensor({multinomial_target_entropy(target_fire_proba)}).unsqueeze(0)) { hard_update(target_critic_1, critic_1); hard_update(target_critic_2, critic_2); diff --git a/arenai_agent/src/agents/sac/sac_trainer.h b/arenai_agent/src/agents/sac/sac_trainer.h index 55d9115c..44b6b8e7 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.h +++ b/arenai_agent/src/agents/sac/sac_trainer.h @@ -23,7 +23,8 @@ namespace arenai::agent { const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, torch::Device device, int metric_window_size, - float tau, float gamma, int train_every, int epochs, int batch_size); + float tau, float gamma, int train_every, int epochs, int batch_size, float target_sigma, + float target_fire_proba); void step() override; diff --git a/arenai_agent/src/networks/actor.cpp b/arenai_agent/src/networks/actor.cpp index d0269755..32e83bc9 100644 --- a/arenai_agent/src/networks/actor.cpp +++ b/arenai_agent/src/networks/actor.cpp @@ -18,7 +18,8 @@ namespace arenai::agent { const int &nb_continuous_actions, const int &nb_discrete_actions, const int &hidden_size_sensors, const std::vector &hidden_sizes, const std::vector> &vision_channels, - const std::vector &group_norm_nums) + const std::vector &group_norm_nums, const float &initial_sigma, + const float &initial_fire_proba) : vision_encoder(register_module( "vision_encoder", std::make_shared( vision_height, vision_width, vision_channels, group_norm_nums))), @@ -64,9 +65,11 @@ namespace arenai::agent { head->apply(init_hidden_weights); mu->apply(init_mu_output_weights); - sigma->apply([](Module &m) { init_sigma_output_weights(m); }); + sigma->apply([initial_sigma](Module &m) { init_sigma_output_weights(m, initial_sigma); }); - discrete->apply([](Module &m) { init_discrete_output_weights(m); }); + discrete->apply([initial_fire_proba](Module &m) { + init_discrete_output_weights(m, initial_fire_proba); + }); } ActorRawOutput Actor::act(const torch::Tensor &vision, const torch::Tensor &sensors) { diff --git a/arenai_agent/src/networks/actor.h b/arenai_agent/src/networks/actor.h index b1442531..549eddcf 100644 --- a/arenai_agent/src/networks/actor.h +++ b/arenai_agent/src/networks/actor.h @@ -26,7 +26,8 @@ namespace arenai::agent { const int &nb_continuous_actions, const int &nb_discrete_actions, const int &hidden_size_sensors, const std::vector &hidden_sizes, const std::vector> &vision_channels, - const std::vector &group_norm_nums); + const std::vector &group_norm_nums, const float &initial_sigma, + const float &initial_fire_proba); ActorRawOutput act(const torch::Tensor &vision, const torch::Tensor &sensors); private: diff --git a/arenai_agent/src/networks/constants.h b/arenai_agent/src/networks/constants.h index a1a5fe9a..5472d6c9 100644 --- a/arenai_agent/src/networks/constants.h +++ b/arenai_agent/src/networks/constants.h @@ -8,9 +8,6 @@ namespace arenai::agent { constexpr float EPSILON = 1e-8f; - constexpr float TARGET_SIGMA = 0.1f; - constexpr float TARGET_FIRE_PROBABILITY = 0.3f; - constexpr float SIGMA_MIN = 1e-4f; constexpr float SIGMA_MAX = 2.f; }// namespace arenai::agent diff --git a/arenai_agent/src/networks_utils/init.h b/arenai_agent/src/networks_utils/init.h index 341512fe..612c234c 100644 --- a/arenai_agent/src/networks_utils/init.h +++ b/arenai_agent/src/networks_utils/init.h @@ -14,9 +14,8 @@ namespace arenai::agent { void init_hidden_weights(torch::nn::Module &module); void init_mu_output_weights(torch::nn::Module &module); - void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma = TARGET_SIGMA); - void init_discrete_output_weights( - torch::nn::Module &module, float initial_fire_probability = TARGET_FIRE_PROBABILITY); + void init_sigma_output_weights(torch::nn::Module &module, float wanted_sigma); + void init_discrete_output_weights(torch::nn::Module &module, float initial_fire_probability); void init_value_output_weights(torch::nn::Module &module); diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index 0833c81e..c13387f9 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -68,7 +68,7 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { const auto actor = std::make_shared( cfg.vision_height, cfg.vision_width, cfg.nb_sensors, cfg.nb_continuous_actions, - cfg.nb_discrete_actions, 8, std::vector{16}, vision_channels, group_norm_nums); + cfg.nb_discrete_actions, 8, std::vector{16}, vision_channels, group_norm_nums, 0.1f, 0.2f); const auto rollout_buffer = std::make_shared(); const auto collector = std::make_shared(rollout_buffer); const auto agent = std::make_shared(actor, device, collector); diff --git a/arenai_agent/tests/src/tests_agents/tests_sac.cpp b/arenai_agent/tests/src/tests_agents/tests_sac.cpp index b75ad29c..a92f991f 100644 --- a/arenai_agent/tests/src/tests_agents/tests_sac.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_sac.cpp @@ -44,8 +44,9 @@ std::unique_ptr SacAgentTest::make_factory(const SacTestCo TorchState SacAgentTest::make_state(const SacTestConfig &cfg, const int batch) { return { - torch::randint(0, 255, {batch, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), - torch::randn({batch, cfg.nb_sensors})}; + .vision = + torch::randint(0, 255, {batch, 3, cfg.vision_height, cfg.vision_width}, torch::kUInt8), + .proprioception = torch::randn({batch, cfg.nb_sensors})}; } // ======================================================================== diff --git a/arenai_agent/tests/src/tests_networks/tests_actor.cpp b/arenai_agent/tests/src/tests_networks/tests_actor.cpp index e4f3c3f5..08c0b673 100644 --- a/arenai_agent/tests/src/tests_networks/tests_actor.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_actor.cpp @@ -20,7 +20,7 @@ TEST_P(ActorTestParam, TestActorAct) { Actor actor( height, width, sensors_nb, cont_actions_nb, discrete_actions_nb, sensors_hidden_size, - layers, {{input_channels, 4}, {4, 8}}, {2, 4}); + layers, {{input_channels, 4}, {4, 8}}, {2, 4}, 0.1f, 0.2f); const auto image = torch::randint( 255, {batch_size, input_channels, height, width}, diff --git a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp index e5a127f8..983873df 100644 --- a/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp +++ b/arenai_agent/tests/src/tests_networks/tests_q_function_consistency.cpp @@ -97,7 +97,7 @@ TEST_F(ActorGradientTest, GradientFlowsThroughActor) { constexpr int nb_sensors = 3, nb_cont = 2, nb_disc = 2; constexpr int batch = 2; - Actor actor(height, width, nb_sensors, nb_cont, nb_disc, 8, {16}, {{3, 4}}, {2}); + Actor actor(height, width, nb_sensors, nb_cont, nb_disc, 8, {16}, {{3, 4}}, {2}, 0.1f, 0.2f); const auto vision = torch::randint(0, 255, {batch, 3, height, width}, torch::kUInt8); const auto sensors = torch::randn({batch, nb_sensors}); @@ -123,7 +123,7 @@ TEST_F(ActorGradientTest, ActorWeightsChangeAfterOptimStep) { constexpr int nb_sensors = 3, nb_cont = 2, nb_disc = 2; constexpr int batch = 2; - Actor actor(height, width, nb_sensors, nb_cont, nb_disc, 8, {16}, {{3, 4}}, {2}); + Actor actor(height, width, nb_sensors, nb_cont, nb_disc, 8, {16}, {{3, 4}}, {2}, 0.1f, 0.2f); auto optimizer = torch::optim::Adam(actor.parameters(), 1e-3); diff --git a/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp b/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp index e9ca62ee..cd83e5c8 100644 --- a/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp +++ b/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp @@ -5,6 +5,7 @@ #include #include +#include #include "./networks/constants.h" #include "./networks/misc.h" @@ -59,7 +60,7 @@ TEST_F(InitWeightsTest, MuOutputBiasZero) { TEST_F(InitWeightsTest, SigmaOutputWeightsOrthogonal) { torch::nn::Linear linear(32, 4); - init_sigma_output_weights(*linear); + init_sigma_output_weights(*linear, 0.f); assert_orthogonal(linear->weight, 0.01f); } @@ -80,11 +81,25 @@ TEST_F(InitWeightsTest, SigmaOutputIsEqualToWantedOne) { TEST_F(InitWeightsTest, DiscreteOutputWeightsOrthogonal) { torch::nn::Linear linear(32, 6); - init_discrete_output_weights(*linear); + init_discrete_output_weights(*linear, 0.f); assert_orthogonal(linear->weight, 0.01f); } +TEST_F(InitWeightsTest, DiscreteOutputFireProbaIsEqualToWantedOne) { + constexpr float wanted_fire_proba = 0.2f; + + torch::nn::Sequential seq( + torch::nn::Linear(32, model::ENEMY_NB_DISCRETE_ACTION), torch::nn::Softmax(-1)); + seq->apply([](torch::nn::Module &m) { init_discrete_output_weights(m, wanted_fire_proba); }); + + torch::Tensor x = torch::randn({1, 32}); + const auto out = seq->forward(x); + + ASSERT_NEAR(out[0][0].item(), wanted_fire_proba, 5e-3f); + ASSERT_NEAR(out[0][1].item(), 1.f - wanted_fire_proba, 5e-3f); +} + TEST_F(InitWeightsTest, ValueOutputWeightsOrthogonal) { torch::nn::Linear linear(32, 1); init_value_output_weights(*linear); From 3cb600cdc66b68ecbbeb80c5f23b3fff18d817be Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Mon, 17 Aug 2026 23:01:48 +0200 Subject: [PATCH 52/70] initialization style --- .../src/agents/ppo/ppo_hyperparams.cpp | 42 ++++++++++--------- 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp index 8445cf8a..e25dd73a 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp @@ -8,26 +8,28 @@ namespace arenai::agent { std::vector> ppo_cli_fields() { return { - {"--actor_learning_rate", &PpoHyperParams::actor_learning_rate}, - {"--critic_learning_rate", &PpoHyperParams::critic_learning_rate}, - {"--hidden_size_sensors", &PpoHyperParams::hidden_size_sensors}, - {"--actor_hidden_sizes", &PpoHyperParams::actor_hidden_sizes}, - {"--critic_hidden_sizes", &PpoHyperParams::critic_hidden_sizes}, - {"--vision_channels", &PpoHyperParams::vision_channels}, - {"--group_norm_nums", &PpoHyperParams::group_norm_nums}, - {"--initial_sigma", &PpoHyperParams::initial_sigma}, - {"--initial_fire_proba", &PpoHyperParams::initial_fire_proba}, - {"--metric_window_size", &PpoHyperParams::metric_window_size}, - {"--gamma", &PpoHyperParams::gamma}, - {"--gae_lambda", &PpoHyperParams::gae_lambda}, - {"--clip_epsilon", &PpoHyperParams::clip_epsilon}, - {"--target_kl", &PpoHyperParams::target_kl}, - {"--grad_norm_max", &PpoHyperParams::grad_norm_max}, - {"--continuous_entropy_coefficient", &PpoHyperParams::continuous_entropy_coefficient}, - {"--discrete_entropy_coefficient", &PpoHyperParams::discrete_entropy_coefficient}, - {"--epochs", &PpoHyperParams::epochs}, - {"--rollout_size", &PpoHyperParams::rollout_size}, - {"--minibatch_size", &PpoHyperParams::minibatch_size}, + {.name = "--actor_learning_rate", .member = &PpoHyperParams::actor_learning_rate}, + {.name = "--critic_learning_rate", .member = &PpoHyperParams::critic_learning_rate}, + {.name = "--hidden_size_sensors", .member = &PpoHyperParams::hidden_size_sensors}, + {.name = "--actor_hidden_sizes", .member = &PpoHyperParams::actor_hidden_sizes}, + {.name = "--critic_hidden_sizes", .member = &PpoHyperParams::critic_hidden_sizes}, + {.name = "--vision_channels", .member = &PpoHyperParams::vision_channels}, + {.name = "--group_norm_nums", .member = &PpoHyperParams::group_norm_nums}, + {.name = "--initial_sigma", .member = &PpoHyperParams::initial_sigma}, + {.name = "--initial_fire_proba", .member = &PpoHyperParams::initial_fire_proba}, + {.name = "--metric_window_size", .member = &PpoHyperParams::metric_window_size}, + {.name = "--gamma", .member = &PpoHyperParams::gamma}, + {.name = "--gae_lambda", .member = &PpoHyperParams::gae_lambda}, + {.name = "--clip_epsilon", .member = &PpoHyperParams::clip_epsilon}, + {.name = "--target_kl", .member = &PpoHyperParams::target_kl}, + {.name = "--grad_norm_max", .member = &PpoHyperParams::grad_norm_max}, + {.name = "--continuous_entropy_coefficient", + .member = &PpoHyperParams::continuous_entropy_coefficient}, + {.name = "--discrete_entropy_coefficient", + .member = &PpoHyperParams::discrete_entropy_coefficient}, + {.name = "--epochs", .member = &PpoHyperParams::epochs}, + {.name = "--rollout_size", .member = &PpoHyperParams::rollout_size}, + {.name = "--minibatch_size", .member = &PpoHyperParams::minibatch_size}, }; } From 816ea671f53dc9957722f2d560b1e8b025fb90a1 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Tue, 18 Aug 2026 16:59:01 +0200 Subject: [PATCH 53/70] fix kl in ppo --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 4 +- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 39 ++++++++++++++++--- .../src/tests_networks_utils/tests_init.cpp | 4 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 2 +- 4 files changed, 38 insertions(+), 11 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 051499e2..656694fc 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -34,8 +34,8 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - float continuous_entropy_coefficient = 2e-3f; - float discrete_entropy_coefficient = 1e-2f; + float continuous_entropy_coefficient = 1e-2f; + float discrete_entropy_coefficient = 2e-3f; int epochs = 4; int rollout_size = 30 * 30; int minibatch_size = 1024; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 351da090..0baec7ab 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -4,6 +4,7 @@ #include "./ppo_trainer.h" +#include #include #include "../../distributions/multinomial.h" @@ -27,6 +28,17 @@ namespace arenai::agent { sizes[0] = tensor.size(0) * tensor.size(1); return tensor.reshape(sizes); } + + // log-ratio guard. Past +-3 the ratio sits two orders of magnitude outside the clip + // range: a row the trust region cannot say anything about anymore. Clamping keeps + // exp() finite, and since clamp has no gradient outside its bounds it also drops + // those rows from the update instead of letting a single one steer it + constexpr float LOG_RATIO_MAX_ABS = 3.f; + + // share of the highest per-row KL dropped before averaging. The threshold has to + // describe the bulk of the minibatch, not its tail: over 1024 rows a single one at + // ratio ~ 40 is worth 0.036 of the mean, more than the whole target_kl + constexpr float KL_TRIM_FRACTION = 0.01f; }// namespace PpoTrainer::PpoTrainer( @@ -144,8 +156,12 @@ namespace arenai::agent { const auto curr_discrete_log_probs = torch::sum(discrete_actions * torch::log(clamped_proba), -1, true); - const auto ratio = - torch::exp(curr_continuous_log_probs + curr_discrete_log_probs - old_log_probs); + // kept as a log-ratio: exp(log_ratio) may underflow to 0, log_ratio itself stays finite + const auto log_ratio = torch::clamp( + curr_continuous_log_probs + curr_discrete_log_probs - old_log_probs, -LOG_RATIO_MAX_ABS, + LOG_RATIO_MAX_ABS); + + const auto ratio = torch::exp(log_ratio); const auto continuous_entropy = truncated_normal_entropy(mu, sigma); @@ -159,8 +175,18 @@ namespace arenai::agent { discrete_entropy_metric->add(discrete_entropy.mean().item()); // approx KL (Schulman): E[(ratio - 1) - log ratio]; skip this minibatch when the - // policy drifted too far from the rollout policy - const auto approx_kl = torch::mean(ratio - 1.f - torch::log(ratio)).item(); + // policy drifted too far from the rollout policy. Averaged over the lowest rows only: + // the estimator is a mean of a heavy-tailed population, and the tail was deciding for + // the whole minibatch (train_349: kl x5 the value its own clip fraction implies) + const auto kl_per_row = (ratio - 1.f - log_ratio).flatten(); + + const auto nb_kept = std::max( + 1, static_cast( + static_cast(kl_per_row.size(0)) * (1.f - KL_TRIM_FRACTION))); + + // per-row KL is non-negative: sorting ascending puts the outliers past nb_kept + const auto approx_kl = + std::get<0>(torch::sort(kl_per_row)).slice(0, 0, nb_kept).mean().item(); kl_metric->add(approx_kl); @@ -171,8 +197,9 @@ namespace arenai::agent { const bool kl_exceeded = target_kl > 0.f && approx_kl > 1.5f * target_kl; - // how much of the update the KL threshold is eating. It is also the only reading left - // when a single underflowed ratio turns the whole kl window into inf + // how much of the update the KL threshold is eating. Also the check on the trim: a + // skip that stays high while kl sits under the threshold means the tail is wider + // than KL_TRIM_FRACTION skip_fraction_metric->add(kl_exceeded ? 1.f : 0.f); if (kl_exceeded) return true; diff --git a/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp b/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp index cd83e5c8..8e7e3862 100644 --- a/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp +++ b/arenai_agent/tests/src/tests_networks_utils/tests_init.cpp @@ -96,8 +96,8 @@ TEST_F(InitWeightsTest, DiscreteOutputFireProbaIsEqualToWantedOne) { torch::Tensor x = torch::randn({1, 32}); const auto out = seq->forward(x); - ASSERT_NEAR(out[0][0].item(), wanted_fire_proba, 5e-3f); - ASSERT_NEAR(out[0][1].item(), 1.f - wanted_fire_proba, 5e-3f); + ASSERT_NEAR(out[0][0].item(), wanted_fire_proba, 1e-2f); + ASSERT_NEAR(out[0][1].item(), 1.f - wanted_fire_proba, 1e-2f); } TEST_F(InitWeightsTest, ValueOutputWeightsOrthogonal) { diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index d6e010a2..04d67ada 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -54,7 +54,7 @@ namespace arenai::model { [this](const std::shared_ptr &shell) { on_shell_fired(shell); }, [this] { return nb_shells > 0; }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), - curr_frame_upside_down(0), miss_distance_scale(1.5f), hit_reward_scale(0.5f), + curr_frame_upside_down(0), miss_distance_scale(6.f), hit_reward_scale(0.5f), hit_received_cost(0.1f), initial_nb_shells(10), nb_shells(initial_nb_shells), shells_recharged_per_hit(5), nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), From 060c89921c8fa3927f68719deec4e02cb1b53c43 Mon Sep 17 00:00:00 2001 From: Samuel Date: Tue, 18 Aug 2026 20:19:59 +0200 Subject: [PATCH 54/70] fix windows build --- arenai_agent/src/core/train_environment.cpp | 1 + arenai_agent/src/metrics/metric.cpp | 1 + arenai_model/include/arenai_model/constants.h | 2 ++ arenai_model/src/items/convex.cpp | 1 + 4 files changed, 5 insertions(+) diff --git a/arenai_agent/src/core/train_environment.cpp b/arenai_agent/src/core/train_environment.cpp index 765a1231..d1c8c014 100644 --- a/arenai_agent/src/core/train_environment.cpp +++ b/arenai_agent/src/core/train_environment.cpp @@ -5,6 +5,7 @@ #include "./train_environment.h" #include +#include #include #include diff --git a/arenai_agent/src/metrics/metric.cpp b/arenai_agent/src/metrics/metric.cpp index fdbae9b3..37c40dbd 100644 --- a/arenai_agent/src/metrics/metric.cpp +++ b/arenai_agent/src/metrics/metric.cpp @@ -6,6 +6,7 @@ #include #include +#include using namespace arenai; using namespace arenai::agent; diff --git a/arenai_model/include/arenai_model/constants.h b/arenai_model/include/arenai_model/constants.h index f1067153..46188a40 100644 --- a/arenai_model/include/arenai_model/constants.h +++ b/arenai_model/include/arenai_model/constants.h @@ -5,6 +5,8 @@ #ifndef ARENAI_MODEL_CONSTANTS_H #define ARENAI_MODEL_CONSTANTS_H +#include + namespace arenai::model { constexpr float WHEEL_RADIAL_VELOCITY = std::numbers::pi * 5.f; diff --git a/arenai_model/src/items/convex.cpp b/arenai_model/src/items/convex.cpp index b12f5a95..13ef5d8c 100644 --- a/arenai_model/src/items/convex.cpp +++ b/arenai_model/src/items/convex.cpp @@ -8,6 +8,7 @@ #include #include +#include #include #include #include From 39da94ffa01bb270762f61e7823bac3f05faee8a Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Tue, 18 Aug 2026 20:44:24 +0200 Subject: [PATCH 55/70] lower continuous entropy coef and lower miss distance scale --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 2 +- arenai_model/src/tank/jolt_enemy_tank.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 656694fc..4f8e642e 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -34,7 +34,7 @@ namespace arenai::agent { // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it float target_kl = 0.02f; float grad_norm_max = 0.5f; - float continuous_entropy_coefficient = 1e-2f; + float continuous_entropy_coefficient = 2e-3f; float discrete_entropy_coefficient = 2e-3f; int epochs = 4; int rollout_size = 30 * 30; diff --git a/arenai_model/src/tank/jolt_enemy_tank.cpp b/arenai_model/src/tank/jolt_enemy_tank.cpp index 04d67ada..d6e010a2 100644 --- a/arenai_model/src/tank/jolt_enemy_tank.cpp +++ b/arenai_model/src/tank/jolt_enemy_tank.cpp @@ -54,7 +54,7 @@ namespace arenai::model { [this](const std::shared_ptr &shell) { on_shell_fired(shell); }, [this] { return nb_shells > 0; }), max_frames_upside_down(static_cast(4.f / wanted_frame_frequency)), - curr_frame_upside_down(0), miss_distance_scale(6.f), hit_reward_scale(0.5f), + curr_frame_upside_down(0), miss_distance_scale(1.5f), hit_reward_scale(0.5f), hit_received_cost(0.1f), initial_nb_shells(10), nb_shells(initial_nb_shells), shells_recharged_per_hit(5), nb_frames_per_shell_regen(static_cast(1.5f / wanted_frame_frequency)), From 1764289b757fedda7adaa61c0a4f3c54419806e9 Mon Sep 17 00:00:00 2001 From: samuel Date: Wed, 19 Aug 2026 00:49:07 +0200 Subject: [PATCH 56/70] Big fix : damages on shell contact --- arenai_agent/src/metrics/metric.cpp | 2 +- arenai_model/src/jolt_engine.cpp | 10 +- arenai_model/src/jolt_engine.h | 2 +- arenai_model/src/tank/parts/shell.cpp | 6 +- .../tests/src/tests_shell/tests_shell.cpp | 120 ++++++++++++++++++ 5 files changed, 132 insertions(+), 8 deletions(-) diff --git a/arenai_agent/src/metrics/metric.cpp b/arenai_agent/src/metrics/metric.cpp index 37c40dbd..6a531064 100644 --- a/arenai_agent/src/metrics/metric.cpp +++ b/arenai_agent/src/metrics/metric.cpp @@ -5,8 +5,8 @@ #include "./metric.h" #include -#include #include +#include using namespace arenai; using namespace arenai::agent; diff --git a/arenai_model/src/jolt_engine.cpp b/arenai_model/src/jolt_engine.cpp index e01a65b8..71705390 100644 --- a/arenai_model/src/jolt_engine.cpp +++ b/arenai_model/src/jolt_engine.cpp @@ -77,16 +77,20 @@ namespace arenai::model { void JoltPhysicEngine::BufferedContactListener::record( const JPH::Body &body1, const JPH::Body &body2, const JPH::ContactManifold &manifold) { - // Bullet fired on_contact once per penetrating manifold point + // speculative contacts are reported before they touch: only penetration counts if (manifold.mPenetrationDepth <= 0.f) return; const auto item_a = reinterpret_cast(body1.GetUserData()); const auto item_b = reinterpret_cast(body2.GetUserData()); if (item_a == nullptr || item_b == nullptr) return; + // one dispatch per touching body pair, never one per manifold point: Jolt + // builds the whole contact patch on the very first frame of an impact, where + // Bullet's persistent manifold grew one point per frame. Iterating the points + // would multiply on_contact — and the damages an impact deals — by however + // many points the clipping happened to produce std::lock_guard lock(contacts_mutex); - for (JPH::uint i = 0; i < manifold.mRelativeContactPointsOn1.size(); i++) - contacts.emplace_back(item_a, item_b); + contacts.emplace_back(item_a, item_b); } void JoltPhysicEngine::BufferedContactListener::OnContactAdded( diff --git a/arenai_model/src/jolt_engine.h b/arenai_model/src/jolt_engine.h index ed5c4ac6..7160a694 100644 --- a/arenai_model/src/jolt_engine.h +++ b/arenai_model/src/jolt_engine.h @@ -75,7 +75,7 @@ namespace arenai::model { float get_interpolation_delta() const; private: - // every step's penetrating contact points, kept until the next simulated + // every step's penetrating body pairs, kept until the next simulated // sub-step so that a step() call that simulates nothing (accumulated time // under the fixed timestep) re-fires the same contacts, like Bullet's // persistent manifolds did diff --git a/arenai_model/src/tank/parts/shell.cpp b/arenai_model/src/tank/parts/shell.cpp index e2ece695..db375d79 100644 --- a/arenai_model/src/tank/parts/shell.cpp +++ b/arenai_model/src/tank/parts/shell.cpp @@ -26,13 +26,13 @@ namespace arenai::model { nb_frames_alive(static_cast(20.f / wanted_frame_frequency)), start_pos(pos) {} void ShellItem::on_contact(Item *other) { + if (is_dead()) return; + if (const auto t = dynamic_cast(other)) t->receive_damages(1); receive_damages(1); Item::on_contact(other); - // the shell identifies itself: the firing tank matches it against its tracked - // shells, two shells fired from the same muzzle pose being indistinguishable - // by position alone + contact_callback(this, get_fire_position(), get_current_position(), other); if (is_dead()) destroy(); diff --git a/arenai_model/tests/src/tests_shell/tests_shell.cpp b/arenai_model/tests/src/tests_shell/tests_shell.cpp index 7eb2fa3d..a02efcc3 100644 --- a/arenai_model/tests/src/tests_shell/tests_shell.cpp +++ b/arenai_model/tests/src/tests_shell/tests_shell.cpp @@ -163,3 +163,123 @@ TEST_F(ShellTest, ShellContactCallbackSetsReward) { const float reward = shared_a->get_reward(tanks); ASSERT_GT(reward, 0.f) << "reward should be positive after shell contact callback"; } + +// ======================================================================== +// ShellItem — damages dealt per impact +// ======================================================================== + +namespace { + + constexpr user_input FIRE_INPUT{ + .left_joystick = {.x = 0.f, .y = 0.f}, + .right_joystick = {.x = 0.f, .y = 0.f}, + .fire_button = {true}}; + + void fire_once(const std::shared_ptr &tank) { + for (const auto &ctrl: tank->get_controllers()) ctrl->apply_input(FIRE_INPUT); + } + + // a tank spreads its health over its parts: its damages are the sum over them + int consume_tank_hits(const std::shared_ptr &tank) { + int hits = 0; + for (const auto &item: tank->get_items()) + if (const auto life_item = dynamic_cast(item.get()); life_item) + hits += life_item->consume_hits_received(); + return hits; + } + + // ShellItem lives behind arenai_model's private Jolt headers: match it by name + std::shared_ptr find_shell(AbstractPhysicEngine &engine) { + for (const auto &item: engine.get_items()) + if (item->get_name() == "shell_item") return item; + return nullptr; + } + +}// namespace + +TEST_F(ShellTest, ShellImpactDealsExactlyOneDamage) { + add_ground(); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + + for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); + + const std::shared_ptr shared_a(tank_a.release()); + const std::shared_ptr shared_b(tank_b.release()); + + consume_tank_hits(shared_b);// drop whatever the settling produced + + fire_once(shared_a); + for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); + + ASSERT_TRUE(shared_a->has_hit_other_tank()) << "shell must hit for this test to mean anything"; + + ASSERT_EQ(consume_tank_hits(shared_b), 1) + << "one shell removes exactly one health point, whatever the number of contact " + "points the physics engine reports for the impact"; +} + +TEST_F(ShellTest, TankSurvivesUntilAPartsHealthPointsAreSpent) { + add_ground(); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + + for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); + + const std::shared_ptr shared_a(tank_a.release()); + const std::shared_ptr shared_b(tank_b.release()); + + consume_tank_hits(shared_b);// drop whatever the settling produced + + // the cheapest part (wheel, turret, canon) is worth 5 health points, and every + // shell removes at most one: no tank dies in fewer than 5 shells, whatever the + // parts they land on + constexpr int cheapest_part_health_points = 5; + constexpr int max_shots = 20; + + int hits = 0; + int shots = 0; + while (!shared_b->is_dead() && shots < max_shots) { + fire_once(shared_a); + for (int i = 0; i < 60; i++) engine->step(1.f / 60.f); + + hits += consume_tank_hits(shared_b); + shots++; + } + + ASSERT_TRUE(shared_b->is_dead()) << "tank should have died within " << max_shots << " shots"; + ASSERT_GE(shots, cheapest_part_health_points) + << "tank died in " << shots << " shells for " << hits + << " health points: a single impact removed more than one"; +} + +TEST_F(ShellTest, SpentShellDealsNoFurtherDamage) { + add_ground(); + auto tank_a = tank_factory->make_enemy_tank(file_reader, "tank_a", {0.f, 5.f, 0.f}); + auto tank_b = tank_factory->make_enemy_tank(file_reader, "tank_b", {0.f, 5.f, 30.f}); + + for (int i = 0; i < 300; i++) engine->step(1.f / 60.f); + + const std::shared_ptr shared_a(tank_a.release()); + const std::shared_ptr shared_b(tank_b.release()); + + fire_once(shared_a); + engine->step(1.f / 60.f); + + const auto shell = find_shell(*engine); + ASSERT_NE(shell, nullptr) << "shell must exist after fire"; + + const auto target = shared_b->get_chassis(); + const auto target_life = dynamic_cast(target.get()); + ASSERT_NE(target_life, nullptr) << "the chassis is a life item"; + + target_life->consume_hits_received(); + + // a shell colliding with several bodies in the same step is dispatched once per + // body: only the first contact may be paid for + shell->on_contact(target.get()); + shell->on_contact(target.get()); + + ASSERT_EQ(target_life->consume_hits_received(), 1) + << "a spent shell must not damage anything again"; +} From c747112be8ee8914f2c33c64029858c247f53c93 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 19 Aug 2026 13:41:56 +0200 Subject: [PATCH 57/70] * fix set train to false (LayerNorm & GroupNorm) * fix PPO hyperparams --- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 25 +++++-- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 75 +++++++------------ arenai_agent/src/agents/ppo/ppo_trainer.h | 2 +- arenai_agent/src/agents/sac/sac_trainer.cpp | 4 + 4 files changed, 53 insertions(+), 53 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 4f8e642e..9ae5bd84 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -25,18 +25,31 @@ namespace arenai::agent { float initial_sigma = 0.5f; float initial_fire_proba = 0.3f; int metric_window_size = 256; - // 0.997 at 30 Hz -> ~11 s credit horizon (shell flight time + fights stay visible) - float gamma = 0.997f; + // 0.99 at 30 Hz -> ~3.3 s bootstrap horizon. 0.997 scaled the returns by 333x the + // per-step reward for a credit horizon gae_lambda already carries: the critic was + // left chasing a high-variance target it never caught (train_353: MSE ~0.22, i.e. + // an RMSE 3x the aim signal reaching the fire decision, so the normalised advantages + // were mostly critic error) + float gamma = 0.99f; // 0.98: a shell resolving 30-60 steps after the fire still reaches the fire - // decision at x0.25-0.5 through GAE, instead of x0.04-0.2 with 0.95 + // decision at x0.16-0.40 through GAE (gamma * lambda = 0.970), instead of + // x0.03-0.16 with 0.95. Raise to 0.99 to get the x0.25-0.50 the 0.997 gamma gave float gae_lambda = 0.98f; float clip_epsilon = 0.2f; - // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it - float target_kl = 0.02f; + // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it. + // 0.05: the clip is already a trust region, this is the second guard. At 0.02 the + // threshold (0.03) sat right on the observed KL distribution and became the binding + // constraint — train_353 skipped 76% of the actor minibatches, and the skip is not a + // neutral subsample: it drops exactly the updates that would move the policy most + float target_kl = 0.05f; float grad_norm_max = 0.5f; float continuous_entropy_coefficient = 2e-3f; float discrete_entropy_coefficient = 2e-3f; - int epochs = 4; + // 2: with 32 tanks x 900 steps / 1024 the rollout is ~29 minibatches, so 4 epochs + // meant 116 sequential Adam steps against a single old_log_probs snapshot. The KL + // grows with the steps taken since that snapshot, so the tail epochs were skipped by + // the actor and applied by the critic alone — 4x the compute for ~1 usable epoch + int epochs = 2; int rollout_size = 30 * 30; int minibatch_size = 1024; }; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index 0baec7ab..b879b768 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -77,6 +77,8 @@ namespace arenai::agent { rollout_size(rollout_size), minibatch_size(minibatch_size) { to(device); + + set_train(false); } void PpoTrainer::step() { @@ -121,24 +123,19 @@ namespace arenai::agent { const auto idx = perm.slice(0, start, std::min(start + minibatch_size, nb_valid_rows)); - // the observation is the bulk of the transfer: uploaded once, used by both const auto mb_vision = select(flat_vision, idx); const auto mb_proprioception = select(flat_proprioception, idx); - // actor: a minibatch past the KL threshold skips its own update only. Latching - // the stop for the rest of the update let a few outlier rows cost the 111 - // other minibatches of the rollout — the recurring cause of the post-peak - // decay of train_341/343/344/346 train_actor( mb_vision, mb_proprioception, select(flat_continuous_actions, idx), select(flat_discrete_actions, idx), select(flat_old_log_probs, idx), select(flat_advantages, idx)); - // critic: keeps training through the whole update, the KL early stop is a - // policy criterion only train_critic(mb_vision, mb_proprioception, select(flat_returns, idx)); } } + + set_train(false); } bool PpoTrainer::train_actor( @@ -146,7 +143,6 @@ namespace arenai::agent { const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions, const torch::Tensor &old_log_probs, const torch::Tensor &advantages) const { - // policy: clipped surrogate on the joint (continuous x discrete) ratio const auto [mu, sigma, discrete_proba] = actor->act(vision, proprioception); const auto curr_continuous_log_probs = @@ -156,7 +152,6 @@ namespace arenai::agent { const auto curr_discrete_log_probs = torch::sum(discrete_actions * torch::log(clamped_proba), -1, true); - // kept as a log-ratio: exp(log_ratio) may underflow to 0, log_ratio itself stays finite const auto log_ratio = torch::clamp( curr_continuous_log_probs + curr_discrete_log_probs - old_log_probs, -LOG_RATIO_MAX_ABS, LOG_RATIO_MAX_ABS); @@ -164,20 +159,8 @@ namespace arenai::agent { const auto ratio = torch::exp(log_ratio); const auto continuous_entropy = truncated_normal_entropy(mu, sigma); - - // as in SAC: summed over the dimensions in the loss, averaged in the metric - continuous_entropy_metric->add(continuous_entropy.mean().item()); - sigma_metric->add(sigma.mean().item()); - - // already summed over the discrete actions: multinomial_entropy keeps the last dim const auto discrete_entropy = multinomial_entropy(discrete_proba); - discrete_entropy_metric->add(discrete_entropy.mean().item()); - - // approx KL (Schulman): E[(ratio - 1) - log ratio]; skip this minibatch when the - // policy drifted too far from the rollout policy. Averaged over the lowest rows only: - // the estimator is a mean of a heavy-tailed population, and the tail was deciding for - // the whole minibatch (train_349: kl x5 the value its own clip fraction implies) const auto kl_per_row = (ratio - 1.f - log_ratio).flatten(); const auto nb_kept = std::max( @@ -188,42 +171,42 @@ namespace arenai::agent { const auto approx_kl = std::get<0>(torch::sort(kl_per_row)).slice(0, 0, nb_kept).mean().item(); - kl_metric->add(approx_kl); - - // recorded before the skip, unlike the actor losses: clip then describes the same - // population of minibatches as kl, whatever the skip rate - clip_fraction_metric->add( - ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); - const bool kl_exceeded = target_kl > 0.f && approx_kl > 1.5f * target_kl; - // how much of the update the KL threshold is eating. Also the check on the trim: a - // skip that stays high while kl sits under the threshold means the tail is wider - // than KL_TRIM_FRACTION - skip_fraction_metric->add(kl_exceeded ? 1.f : 0.f); - - if (kl_exceeded) return true; + const auto entropy_bonus = + continuous_entropy_coefficient * torch::sum(continuous_entropy, -1, true) + + discrete_entropy_coefficient * discrete_entropy; - const auto clipped_ratio = torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); + torch::Tensor actor_loss = -torch::mean(entropy_bonus); - const auto surrogate = torch::min(ratio * advantages, clipped_ratio * advantages); + if (!kl_exceeded) { + const auto clipped_ratio = torch::clamp(ratio, 1.f - clip_epsilon, 1.f + clip_epsilon); + const auto surrogate = torch::min(ratio * advantages, clipped_ratio * advantages); - // the bonuses are the only upward force on the entropies: the surrogate shrinks both - // on its own, and neither head has a restoring force of its own - const auto actor_loss = -torch::mean( - surrogate + continuous_entropy_coefficient * torch::sum(continuous_entropy, -1, true) - + discrete_entropy_coefficient * discrete_entropy); + actor_loss -= torch::mean(surrogate + entropy_bonus); + } actor_optim->zero_grad(); actor_loss.backward(); torch::nn::utils::clip_grad_norm_(actor->parameters(), grad_norm_max); actor_optim->step(); - const auto loss_value = actor_loss.cpu().item(); - actor_mean_loss_metric->add(loss_value); - actor_std_loss_metric->add(loss_value); + // metrics + if (!kl_exceeded) { + const auto loss_value = actor_loss.cpu().item(); + actor_mean_loss_metric->add(loss_value); + actor_std_loss_metric->add(loss_value); + } + + continuous_entropy_metric->add(continuous_entropy.mean().item()); + sigma_metric->add(sigma.mean().item()); + discrete_entropy_metric->add(discrete_entropy.mean().item()); + kl_metric->add(approx_kl); + clip_fraction_metric->add( + ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); + skip_fraction_metric->add(kl_exceeded ? 1.f : 0.f); - return false; + return kl_exceeded; } void PpoTrainer::train_critic( @@ -334,7 +317,7 @@ namespace arenai::agent { critic->train(train); } - void PpoTrainer::to(const torch::Device device) { + void PpoTrainer::to(const torch::Device device) const { actor->to(device); critic->to(device); } diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index b4e27109..16a6385e 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -104,7 +104,7 @@ namespace arenai::agent { GaeResult compute_gae(const PpoRollout &rollout, torch::Device device) const; void set_train(bool train) const; - void to(torch::Device device); + void to(torch::Device device) const; }; }// namespace arenai::agent diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 9de12ec1..31bfd3de 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -87,6 +87,8 @@ namespace arenai::agent { hard_update(target_critic_2, critic_2); to(device); + + set_train(false); } void SacTrainer::step() { @@ -232,6 +234,8 @@ namespace arenai::agent { alpha_continuous_metric->add(alpha_continuous->alpha().mean().item()); alpha_discrete_metric->add(alpha_discrete->alpha().mean().item()); } + + set_train(false); } std::vector> SacTrainer::get_metrics() { From 50429810880fad7bb9f96cf0a0432cf38cca6943 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 19 Aug 2026 19:55:50 +0200 Subject: [PATCH 58/70] add critic ev metric --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 18 +++++++++++++++--- arenai_agent/src/agents/ppo/ppo_trainer.h | 3 +++ .../tests/src/tests_agents/tests_ppo.cpp | 2 +- 3 files changed, 19 insertions(+), 4 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index b879b768..cae9dc3c 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -64,6 +64,7 @@ namespace arenai::agent { actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), critic_std_loss_metric(std::make_shared("v_σ", metric_window_size)), + explained_variance_metric(std::make_shared("ev", metric_window_size)), continuous_entropy_metric(std::make_shared("Hc", metric_window_size)), discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), sigma_metric(std::make_shared("σ", metric_window_size)), @@ -224,6 +225,15 @@ namespace arenai::agent { const auto loss_value = critic_loss.cpu().item(); critic_mean_loss_metric->add(loss_value); critic_std_loss_metric->add(loss_value); + + // explained variance of the forward the step was computed from: the share of the + // return variance the critic accounts for. 1 = perfect fit, <= 0 = no better than + // predicting the mean return — the residual v_μ cannot tell those apart on its own + // since it also moves with the scale of the returns + const auto residual_var = (returns - values.detach()).var(false); + const auto returns_var = returns.var(false); + explained_variance_metric->add( + (1.f - residual_var / returns_var.clamp_min(EPSILON)).item()); } GaeResult PpoTrainer::compute_gae(const PpoRollout &rollout, const torch::Device device) const { @@ -283,9 +293,11 @@ namespace arenai::agent { } std::vector> PpoTrainer::get_metrics() { - return {actor_mean_loss_metric, actor_std_loss_metric, critic_mean_loss_metric, - critic_std_loss_metric, continuous_entropy_metric, discrete_entropy_metric, - sigma_metric, clip_fraction_metric, kl_metric, + return {actor_mean_loss_metric, actor_std_loss_metric, + critic_mean_loss_metric, critic_std_loss_metric, + explained_variance_metric, continuous_entropy_metric, + discrete_entropy_metric, sigma_metric, + clip_fraction_metric, kl_metric, skip_fraction_metric}; } diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index 16a6385e..c09b0a71 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -53,6 +53,9 @@ namespace arenai::agent { std::shared_ptr critic_mean_loss_metric; std::shared_ptr critic_std_loss_metric; + // share of the return variance the critic explains, per critic minibatch + std::shared_ptr explained_variance_metric; + // both regulated by their constant entropy bonus std::shared_ptr continuous_entropy_metric; std::shared_ptr discrete_entropy_metric; diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index 341cb496..ba267efa 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -75,7 +75,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 10); + ASSERT_EQ(metrics.size(), 11); } // ======================================================================== From 5f41fc18ca69113cf5226805e255cba5db829a2a Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 19 Aug 2026 22:43:25 +0200 Subject: [PATCH 59/70] re-add alpha parameters for PPO => per continuous action alpha --- arenai_agent/src/agents/ppo/ppo_factory.cpp | 12 +-- .../src/agents/ppo/ppo_hyperparams.cpp | 7 +- arenai_agent/src/agents/ppo/ppo_hyperparams.h | 24 +---- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 88 ++++++++++++++----- arenai_agent/src/agents/ppo/ppo_trainer.h | 29 ++++-- .../tests/src/tests_agents/tests_ppo.cpp | 2 +- .../src/tests_agents/tests_ppo_training.cpp | 28 ++++-- 7 files changed, 122 insertions(+), 68 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_factory.cpp b/arenai_agent/src/agents/ppo/ppo_factory.cpp index fa7130fb..23b5f3af 100644 --- a/arenai_agent/src/agents/ppo/ppo_factory.cpp +++ b/arenai_agent/src/agents/ppo/ppo_factory.cpp @@ -21,12 +21,12 @@ namespace arenai::agent { collector(std::make_shared(rollout_buffer)), agent(std::make_shared(actor, device, collector)), trainer(std::make_shared( - actor, rollout_buffer, vision_height, vision_width, nb_sensors, - params.actor_learning_rate, params.critic_learning_rate, params.hidden_size_sensors, - params.critic_hidden_sizes, params.vision_channels, params.group_norm_nums, device, - params.metric_window_size, params.gamma, params.gae_lambda, params.clip_epsilon, - params.target_kl, params.grad_norm_max, params.continuous_entropy_coefficient, - params.discrete_entropy_coefficient, params.epochs, params.rollout_size, + actor, rollout_buffer, vision_height, vision_width, nb_sensors, nb_continuous_actions, + params.actor_learning_rate, params.critic_learning_rate, params.alpha_learning_rate, + params.hidden_size_sensors, params.critic_hidden_sizes, params.vision_channels, + params.group_norm_nums, device, params.metric_window_size, params.gamma, + params.gae_lambda, params.clip_epsilon, params.target_kl, params.grad_norm_max, + params.target_sigma, params.target_fire_proba, params.epochs, params.rollout_size, params.minibatch_size)) {} std::shared_ptr PpoTorchAgentFactory::get_agent() { return agent; } diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp index e25dd73a..ef97e2f3 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.cpp @@ -10,6 +10,7 @@ namespace arenai::agent { return { {.name = "--actor_learning_rate", .member = &PpoHyperParams::actor_learning_rate}, {.name = "--critic_learning_rate", .member = &PpoHyperParams::critic_learning_rate}, + {.name = "--alpha_learning_rate", .member = &PpoHyperParams::alpha_learning_rate}, {.name = "--hidden_size_sensors", .member = &PpoHyperParams::hidden_size_sensors}, {.name = "--actor_hidden_sizes", .member = &PpoHyperParams::actor_hidden_sizes}, {.name = "--critic_hidden_sizes", .member = &PpoHyperParams::critic_hidden_sizes}, @@ -23,10 +24,8 @@ namespace arenai::agent { {.name = "--clip_epsilon", .member = &PpoHyperParams::clip_epsilon}, {.name = "--target_kl", .member = &PpoHyperParams::target_kl}, {.name = "--grad_norm_max", .member = &PpoHyperParams::grad_norm_max}, - {.name = "--continuous_entropy_coefficient", - .member = &PpoHyperParams::continuous_entropy_coefficient}, - {.name = "--discrete_entropy_coefficient", - .member = &PpoHyperParams::discrete_entropy_coefficient}, + {.name = "--target_sigma", .member = &PpoHyperParams::target_sigma}, + {.name = "--target_fire_proba", .member = &PpoHyperParams::target_fire_proba}, {.name = "--epochs", .member = &PpoHyperParams::epochs}, {.name = "--rollout_size", .member = &PpoHyperParams::rollout_size}, {.name = "--minibatch_size", .member = &PpoHyperParams::minibatch_size}, diff --git a/arenai_agent/src/agents/ppo/ppo_hyperparams.h b/arenai_agent/src/agents/ppo/ppo_hyperparams.h index 9ae5bd84..d44ad36d 100644 --- a/arenai_agent/src/agents/ppo/ppo_hyperparams.h +++ b/arenai_agent/src/agents/ppo/ppo_hyperparams.h @@ -16,6 +16,7 @@ namespace arenai::agent { struct PpoHyperParams { float actor_learning_rate = 1e-4f; float critic_learning_rate = 3e-4f; + float alpha_learning_rate = 2e-4f; int hidden_size_sensors = 128; std::vector actor_hidden_sizes = {1024, 512}; std::vector critic_hidden_sizes = {1024, 512}; @@ -23,32 +24,15 @@ namespace arenai::agent { {24, 32}, {32, 48}, {48, 64}}; std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; float initial_sigma = 0.5f; - float initial_fire_proba = 0.3f; + float initial_fire_proba = 0.4f; int metric_window_size = 256; - // 0.99 at 30 Hz -> ~3.3 s bootstrap horizon. 0.997 scaled the returns by 333x the - // per-step reward for a credit horizon gae_lambda already carries: the critic was - // left chasing a high-variance target it never caught (train_353: MSE ~0.22, i.e. - // an RMSE 3x the aim signal reaching the fire decision, so the normalised advantages - // were mostly critic error) float gamma = 0.99f; - // 0.98: a shell resolving 30-60 steps after the fire still reaches the fire - // decision at x0.16-0.40 through GAE (gamma * lambda = 0.970), instead of - // x0.03-0.16 with 0.95. Raise to 0.99 to get the x0.25-0.50 the 0.997 gamma gave float gae_lambda = 0.98f; float clip_epsilon = 0.2f; - // early-stop of the epoch loop when approx KL > 1.5 * target_kl; <= 0 disables it. - // 0.05: the clip is already a trust region, this is the second guard. At 0.02 the - // threshold (0.03) sat right on the observed KL distribution and became the binding - // constraint — train_353 skipped 76% of the actor minibatches, and the skip is not a - // neutral subsample: it drops exactly the updates that would move the policy most float target_kl = 0.05f; float grad_norm_max = 0.5f; - float continuous_entropy_coefficient = 2e-3f; - float discrete_entropy_coefficient = 2e-3f; - // 2: with 32 tanks x 900 steps / 1024 the rollout is ~29 minibatches, so 4 epochs - // meant 116 sequential Adam steps against a single old_log_probs snapshot. The KL - // grows with the steps taken since that snapshot, so the tail epochs were skipped by - // the actor and applied by the critic alone — 4x the compute for ~1 usable epoch + float target_sigma = 0.1f; + float target_fire_proba = 0.05f; int epochs = 2; int rollout_size = 30 * 30; int minibatch_size = 1024; diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index cae9dc3c..b1e57942 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -19,7 +19,6 @@ using namespace arenai; using namespace arenai::agent; namespace arenai::agent { - namespace { // merges the [T, nb_tanks] leading dimensions into a single row dimension torch::Tensor flatten_steps(const torch::Tensor &tensor) { @@ -42,24 +41,32 @@ namespace arenai::agent { }// namespace PpoTrainer::PpoTrainer( - std::shared_ptr actor, std::shared_ptr rollout_buffer, - const int vision_height, const int vision_width, const int nb_sensors, + const std::shared_ptr &actor, + const std::shared_ptr &rollout_buffer, const int vision_height, + const int vision_width, const int nb_sensors, const int nb_continuous_actions, const float actor_learning_rate, const float critic_learning_rate, - const int hidden_size_sensors, const std::vector &critic_hidden_sizes, + float alpha_learning_rate, const int hidden_size_sensors, + const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, const torch::Device device, const int metric_window_size, const float gamma, const float gae_lambda, const float clip_epsilon, const float target_kl, const float grad_norm_max, - const float continuous_entropy_coefficient, const float discrete_entropy_coefficient, - const int epochs, const int rollout_size, const int minibatch_size) - : actor(std::move(actor)), rollout_buffer(std::move(rollout_buffer)), + const float target_sigma, const float target_fire_proba, const int epochs, + const int rollout_size, const int minibatch_size) + : actor(actor), rollout_buffer(rollout_buffer), + continuous_alpha(std::make_shared(1e-3f, nb_continuous_actions)), + discrete_alpha(std::make_shared(1e-3f, 1)), critic(std::make_shared( vision_height, vision_width, nb_sensors, hidden_size_sensors, critic_hidden_sizes, vision_channels, group_norm_nums)), - actor_optim(std::make_unique( - this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), - critic_optim(std::make_unique( - critic->parameters(), torch::optim::AdamOptions(critic_learning_rate))), + actor_optim( + std::make_unique(this->actor->parameters(), actor_learning_rate)), + critic_optim( + std::make_unique(critic->parameters(), critic_learning_rate)), + continuous_alpha_optim(std::make_unique( + continuous_alpha->parameters(), alpha_learning_rate)), + discrete_alpha_optim(std::make_unique( + discrete_alpha->parameters(), alpha_learning_rate)), actor_mean_loss_metric(std::make_shared("π_μ", metric_window_size)), actor_std_loss_metric(std::make_shared("π_σ", metric_window_size)), critic_mean_loss_metric(std::make_shared("v_μ", metric_window_size)), @@ -67,16 +74,16 @@ namespace arenai::agent { explained_variance_metric(std::make_shared("ev", metric_window_size)), continuous_entropy_metric(std::make_shared("Hc", metric_window_size)), discrete_entropy_metric(std::make_shared("Hd", metric_window_size)), + continuous_alpha_metric(std::make_shared("α_c", metric_window_size, 2, true)), + discrete_alpha_metric(std::make_shared("α_d", metric_window_size, 2, true)), sigma_metric(std::make_shared("σ", metric_window_size)), clip_fraction_metric(std::make_shared("clip", metric_window_size)), kl_metric(std::make_shared("kl", metric_window_size, 2, true)), skip_fraction_metric(std::make_shared("skip", metric_window_size)), gamma(gamma), gae_lambda(gae_lambda), clip_epsilon(clip_epsilon), target_kl(target_kl), - grad_norm_max(grad_norm_max), - continuous_entropy_coefficient(continuous_entropy_coefficient), - discrete_entropy_coefficient(discrete_entropy_coefficient), epochs(epochs), - rollout_size(rollout_size), minibatch_size(minibatch_size) { - + grad_norm_max(grad_norm_max), target_sigma(target_sigma), + target_fire_proba(target_fire_proba), epochs(epochs), rollout_size(rollout_size), + minibatch_size(minibatch_size) { to(device); set_train(false); @@ -143,7 +150,6 @@ namespace arenai::agent { const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &continuous_actions, const torch::Tensor &discrete_actions, const torch::Tensor &old_log_probs, const torch::Tensor &advantages) const { - const auto [mu, sigma, discrete_proba] = actor->act(vision, proprioception); const auto curr_continuous_log_probs = @@ -175,8 +181,8 @@ namespace arenai::agent { const bool kl_exceeded = target_kl > 0.f && approx_kl > 1.5f * target_kl; const auto entropy_bonus = - continuous_entropy_coefficient * torch::sum(continuous_entropy, -1, true) - + discrete_entropy_coefficient * discrete_entropy; + torch::sum(continuous_alpha->alpha().detach() * continuous_entropy, -1) + + discrete_alpha->alpha().squeeze(1).detach() * discrete_entropy; torch::Tensor actor_loss = -torch::mean(entropy_bonus); @@ -192,6 +198,33 @@ namespace arenai::agent { torch::nn::utils::clip_grad_norm_(actor->parameters(), grad_norm_max); actor_optim->step(); + // train continuous alpha + const auto target_sigma_tensor = torch::ones_like(continuous_entropy) * target_sigma; + const auto target_continuous_entropy = + truncated_normal_entropy(mu.detach(), target_sigma_tensor); + + const auto continuous_alpha_loss = + torch::sum( + continuous_alpha->log_alpha() + * torch::detach(continuous_entropy - target_continuous_entropy), + -1) + .mean(); + + continuous_alpha_optim->zero_grad(); + continuous_alpha_loss.backward(); + continuous_alpha_optim->step(); + + // train discrete alpha + const auto target_discrete_entropy = multinomial_target_entropy(target_fire_proba); + + const auto discrete_alpha_loss = torch::mean( + discrete_alpha->log_alpha().squeeze(1) + * torch::detach(discrete_entropy - target_discrete_entropy)); + + discrete_alpha_optim->zero_grad(); + discrete_alpha_loss.backward(); + discrete_alpha_optim->step(); + // metrics if (!kl_exceeded) { const auto loss_value = actor_loss.cpu().item(); @@ -201,10 +234,16 @@ namespace arenai::agent { continuous_entropy_metric->add(continuous_entropy.mean().item()); sigma_metric->add(sigma.mean().item()); + continuous_alpha_metric->add(continuous_alpha->alpha().mean().item()); + discrete_entropy_metric->add(discrete_entropy.mean().item()); + discrete_alpha_metric->add(discrete_alpha->alpha().mean().item()); + kl_metric->add(approx_kl); + clip_fraction_metric->add( ((ratio - 1.f).abs() > clip_epsilon).to(torch::kFloat).mean().item()); + skip_fraction_metric->add(kl_exceeded ? 1.f : 0.f); return kl_exceeded; @@ -213,7 +252,6 @@ namespace arenai::agent { void PpoTrainer::train_critic( const torch::Tensor &vision, const torch::Tensor &proprioception, const torch::Tensor &returns) const { - const auto values = critic->value(vision, proprioception); const auto critic_loss = torch::mse_loss(values, returns, at::Reduction::Mean); @@ -296,7 +334,8 @@ namespace arenai::agent { return {actor_mean_loss_metric, actor_std_loss_metric, critic_mean_loss_metric, critic_std_loss_metric, explained_variance_metric, continuous_entropy_metric, - discrete_entropy_metric, sigma_metric, + continuous_alpha_metric, sigma_metric, + discrete_entropy_metric, discrete_alpha_metric, clip_fraction_metric, kl_metric, skip_fraction_metric}; } @@ -327,16 +366,21 @@ namespace arenai::agent { void PpoTrainer::set_train(const bool train) const { actor->train(train); critic->train(train); + + continuous_alpha->train(train); + discrete_alpha->train(train); } void PpoTrainer::to(const torch::Device device) const { actor->to(device); critic->to(device); + + continuous_alpha->to(device); + discrete_alpha->to(device); } int PpoTrainer::count_parameters() { return count_parameters_impl(actor->parameters()) + count_parameters_impl(critic->parameters()); } - }// namespace arenai::agent diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.h b/arenai_agent/src/agents/ppo/ppo_trainer.h index c09b0a71..d89e99c9 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.h +++ b/arenai_agent/src/agents/ppo/ppo_trainer.h @@ -6,6 +6,7 @@ #define ARENAI_PPO_TRAINER_H #include "../../networks/actor.h" +#include "../../networks/entropy.h" #include "../../networks/value_function.h" #include "../trainer.h" #include "./ppo_rollout_buffer.h" @@ -20,15 +21,16 @@ namespace arenai::agent { class PpoTrainer final : public AbstractTrainer { public: PpoTrainer( - std::shared_ptr actor, std::shared_ptr rollout_buffer, - int vision_height, int vision_width, int nb_sensors, float actor_learning_rate, - float critic_learning_rate, int hidden_size_sensors, + const std::shared_ptr &actor, + const std::shared_ptr &rollout_buffer, int vision_height, + int vision_width, int nb_sensors, int nb_continuous_actions, float actor_learning_rate, + float critic_learning_rate, float alpha_learning_rate, int hidden_size_sensors, const std::vector &critic_hidden_sizes, const std::vector> &vision_channels, const std::vector &group_norm_nums, torch::Device device, int metric_window_size, float gamma, float gae_lambda, float clip_epsilon, float target_kl, float grad_norm_max, - float continuous_entropy_coefficient, float discrete_entropy_coefficient, int epochs, - int rollout_size, int minibatch_size); + float target_sigma, float target_fire_proba, int epochs, int rollout_size, + int minibatch_size); void step() override; @@ -42,10 +44,16 @@ namespace arenai::agent { std::shared_ptr actor; std::shared_ptr rollout_buffer; + std::shared_ptr continuous_alpha; + std::shared_ptr discrete_alpha; + std::shared_ptr critic; - std::shared_ptr actor_optim; - std::shared_ptr critic_optim; + std::unique_ptr actor_optim; + std::unique_ptr critic_optim; + + std::unique_ptr continuous_alpha_optim; + std::unique_ptr discrete_alpha_optim; std::shared_ptr actor_mean_loss_metric; std::shared_ptr actor_std_loss_metric; @@ -60,6 +68,9 @@ namespace arenai::agent { std::shared_ptr continuous_entropy_metric; std::shared_ptr discrete_entropy_metric; + std::shared_ptr continuous_alpha_metric; + std::shared_ptr discrete_alpha_metric; + // mean sigma of the truncated normal: direct view of the aim spread, Hc only bounds it std::shared_ptr sigma_metric; @@ -78,8 +89,8 @@ namespace arenai::agent { float grad_norm_max; - float continuous_entropy_coefficient; - float discrete_entropy_coefficient; + float target_sigma; + float target_fire_proba; int epochs; // rollout horizon: one train() consumes rollout_size complete steps in a single update diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp index ba267efa..640f392f 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo.cpp @@ -75,7 +75,7 @@ TEST_F(PpoAgentTest, MetricsNotEmpty) { const auto metrics = factory->get_trainer()->get_metrics(); - ASSERT_EQ(metrics.size(), 11); + ASSERT_EQ(metrics.size(), 13); } // ======================================================================== diff --git a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp index c13387f9..785df36e 100644 --- a/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp +++ b/arenai_agent/tests/src/tests_agents/tests_ppo_training.cpp @@ -37,7 +37,12 @@ TorchState PpoTrainingTest::make_state(const PpoTrainingTestConfig &cfg, const i } TEST_F(PpoTrainingTest, ActProducesValidOutput) { - constexpr PpoTrainingTestConfig cfg{8, 8, 3, 2, 3}; + constexpr PpoTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; const auto factory = make_factory(cfg); const auto [continuous_action, discrete_action] = factory->get_agent()->act(make_state(cfg, 1)); @@ -52,7 +57,12 @@ TEST_F(PpoTrainingTest, ActProducesValidOutput) { } TEST_F(PpoTrainingTest, CountParametersPositive) { - constexpr PpoTrainingTestConfig cfg{8, 8, 3, 2, 3}; + constexpr PpoTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; const auto factory = make_factory(cfg); ASSERT_GT(factory->get_trainer()->count_parameters(), 0) @@ -61,7 +71,12 @@ TEST_F(PpoTrainingTest, CountParametersPositive) { TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { // build the triad by hand to keep a handle on the actor's parameters - constexpr PpoTrainingTestConfig cfg{8, 8, 3, 2, 3}; + constexpr PpoTrainingTestConfig cfg{ + .vision_height = 8, + .vision_width = 8, + .nb_sensors = 3, + .nb_continuous_actions = 2, + .nb_discrete_actions = 3}; const std::vector> vision_channels{{3, 4}}; const std::vector group_norm_nums{2}; @@ -74,9 +89,10 @@ TEST_F(PpoTrainingTest, TrainingUpdatesActorParameters) { const auto agent = std::make_shared(actor, device, collector); // target_kl = 0 : early stop disabled so every minibatch applies its update const auto trainer = std::make_shared( - actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, 1e-3f, 1e-3f, 8, - std::vector{16}, vision_channels, group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, 1.f, - 2e-3f, 2e-3f, 2, ROLLOUT_SIZE, MINIBATCH_SIZE); + actor, rollout_buffer, cfg.vision_height, cfg.vision_width, cfg.nb_sensors, + cfg.nb_continuous_actions, 1e-3f, 1e-3f, 1e-3f, 8, std::vector{16}, vision_channels, + group_norm_nums, device, 10, 0.99f, 0.95f, 0.2f, 0.f, 1.f, 2e-3f, 2e-3f, 2, ROLLOUT_SIZE, + MINIBATCH_SIZE); std::vector initial_parameters; for (const auto ¶meter: actor->parameters()) From 12f3fe0ee5980859648f2c3c673113354150164b Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Wed, 19 Aug 2026 22:45:32 +0200 Subject: [PATCH 60/70] remove comment --- arenai_agent/src/agents/ppo/ppo_trainer.cpp | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/arenai_agent/src/agents/ppo/ppo_trainer.cpp b/arenai_agent/src/agents/ppo/ppo_trainer.cpp index b1e57942..b82817da 100644 --- a/arenai_agent/src/agents/ppo/ppo_trainer.cpp +++ b/arenai_agent/src/agents/ppo/ppo_trainer.cpp @@ -28,15 +28,8 @@ namespace arenai::agent { return tensor.reshape(sizes); } - // log-ratio guard. Past +-3 the ratio sits two orders of magnitude outside the clip - // range: a row the trust region cannot say anything about anymore. Clamping keeps - // exp() finite, and since clamp has no gradient outside its bounds it also drops - // those rows from the update instead of letting a single one steer it constexpr float LOG_RATIO_MAX_ABS = 3.f; - // share of the highest per-row KL dropped before averaging. The threshold has to - // describe the bulk of the minibatch, not its tail: over 1024 rows a single one at - // ratio ~ 40 is worth 0.036 of the mean, more than the whole target_kl constexpr float KL_TRIM_FRACTION = 0.01f; }// namespace @@ -264,10 +257,6 @@ namespace arenai::agent { critic_mean_loss_metric->add(loss_value); critic_std_loss_metric->add(loss_value); - // explained variance of the forward the step was computed from: the share of the - // return variance the critic accounts for. 1 = perfect fit, <= 0 = no better than - // predicting the mean return — the residual v_μ cannot tell those apart on its own - // since it also moves with the scale of the returns const auto residual_var = (returns - values.detach()).var(false); const auto returns_var = returns.var(false); explained_variance_metric->add( From 2acc712135db6577dd501681044be072dc31d199 Mon Sep 17 00:00:00 2001 From: samuel Date: Wed, 19 Aug 2026 14:20:17 +0200 Subject: [PATCH 61/70] update SAC hyperparams --- arenai_agent/src/agents/sac/sac_hyperparams.h | 4 ++-- arenai_agent/src/agents/sac/sac_trainer.cpp | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_hyperparams.h b/arenai_agent/src/agents/sac/sac_hyperparams.h index b215b4a9..a32edbe5 100644 --- a/arenai_agent/src/agents/sac/sac_hyperparams.h +++ b/arenai_agent/src/agents/sac/sac_hyperparams.h @@ -24,8 +24,8 @@ namespace arenai::agent { std::vector> vision_channels = {{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}; std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; - float target_sigma = 0.1f; - float target_fire_proba = 0.05f; + float target_sigma = 0.3f; + float target_fire_proba = 0.2f; int metric_window_size = 256; float tau = 0.005f; float gamma = 0.997f; diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index 31bfd3de..a5e3df21 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -51,9 +51,9 @@ namespace arenai::agent { vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, hidden_size_sensors, hidden_size_actions, critic_hidden_sizes, vision_channels, group_norm_nums)), - alpha_continuous(std::make_shared( - 0.001f, 1e-5f, 1e-2f, nb_continuous_actions)), - alpha_discrete(std::make_shared(0.001f, 1e-5f, 1e-2f, 1)), + alpha_continuous( + std::make_shared(1e-2f, 1e-5f, 1e-1f, nb_continuous_actions)), + alpha_discrete(std::make_shared(1e-2f, 1e-5f, 1e-1f, 1)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_1_optim(std::make_unique( From 8fdb9ce40efbff14672287a0fee5ae0a73bc1005 Mon Sep 17 00:00:00 2001 From: samuel Date: Wed, 19 Aug 2026 23:07:13 +0200 Subject: [PATCH 62/70] initial sigma for SAC --- arenai_agent/src/agents/sac/sac_factory.cpp | 2 +- arenai_agent/src/agents/sac/sac_hyperparams.cpp | 2 ++ arenai_agent/src/agents/sac/sac_hyperparams.h | 8 +++++--- arenai_agent/src/agents/sac/sac_trainer.cpp | 4 ++-- 4 files changed, 10 insertions(+), 6 deletions(-) diff --git a/arenai_agent/src/agents/sac/sac_factory.cpp b/arenai_agent/src/agents/sac/sac_factory.cpp index 96b4a091..0e5e5ade 100644 --- a/arenai_agent/src/agents/sac/sac_factory.cpp +++ b/arenai_agent/src/agents/sac/sac_factory.cpp @@ -16,7 +16,7 @@ namespace arenai::agent { : actor(std::make_shared( vision_height, vision_width, nb_sensors, nb_continuous_actions, nb_discrete_actions, params.hidden_size_sensors, params.actor_hidden_sizes, params.vision_channels, - params.group_norm_nums, params.target_sigma, params.target_fire_proba)), + params.group_norm_nums, params.initial_sigma, params.initial_fire_proba)), replay_buffer(std::make_shared(params.replay_buffer_size)), collector(std::make_shared(replay_buffer)), agent(std::make_shared(actor, device, collector)), diff --git a/arenai_agent/src/agents/sac/sac_hyperparams.cpp b/arenai_agent/src/agents/sac/sac_hyperparams.cpp index 198161d1..4b12bf0a 100644 --- a/arenai_agent/src/agents/sac/sac_hyperparams.cpp +++ b/arenai_agent/src/agents/sac/sac_hyperparams.cpp @@ -17,6 +17,8 @@ namespace arenai::agent { {.name = "--critic_hidden_sizes", .member = &SacHyperParams::critic_hidden_sizes}, {.name = "--vision_channels", .member = &SacHyperParams::vision_channels}, {.name = "--group_norm_nums", .member = &SacHyperParams::group_norm_nums}, + {.name = "--initial_sigma", .member = &SacHyperParams::initial_sigma}, + {.name = "--initial_fire_proba", .member = &SacHyperParams::initial_fire_proba}, {.name = "--target_sigma", .member = &SacHyperParams::target_sigma}, {.name = "--target_fire_proba", .member = &SacHyperParams::target_fire_proba}, {.name = "--metric_window_size", .member = &SacHyperParams::metric_window_size}, diff --git a/arenai_agent/src/agents/sac/sac_hyperparams.h b/arenai_agent/src/agents/sac/sac_hyperparams.h index a32edbe5..e57c5eae 100644 --- a/arenai_agent/src/agents/sac/sac_hyperparams.h +++ b/arenai_agent/src/agents/sac/sac_hyperparams.h @@ -24,11 +24,13 @@ namespace arenai::agent { std::vector> vision_channels = {{3, 8}, {8, 16}, {16, 24}, {24, 32}, {32, 48}, {48, 64}}; std::vector group_norm_nums = {1, 2, 3, 4, 6, 8}; - float target_sigma = 0.3f; - float target_fire_proba = 0.2f; + float initial_sigma = 0.5f; + float initial_fire_proba = 0.4f; + float target_sigma = 0.1f; + float target_fire_proba = 0.05f; int metric_window_size = 256; float tau = 0.005f; - float gamma = 0.997f; + float gamma = 0.99f; int replay_buffer_size = 300000; int train_every = 256; int epochs = 128; diff --git a/arenai_agent/src/agents/sac/sac_trainer.cpp b/arenai_agent/src/agents/sac/sac_trainer.cpp index a5e3df21..133d970c 100644 --- a/arenai_agent/src/agents/sac/sac_trainer.cpp +++ b/arenai_agent/src/agents/sac/sac_trainer.cpp @@ -52,8 +52,8 @@ namespace arenai::agent { hidden_size_sensors, hidden_size_actions, critic_hidden_sizes, vision_channels, group_norm_nums)), alpha_continuous( - std::make_shared(1e-2f, 1e-5f, 1e-1f, nb_continuous_actions)), - alpha_discrete(std::make_shared(1e-2f, 1e-5f, 1e-1f, 1)), + std::make_shared(5e-2f, 1e-5f, 1e-1f, nb_continuous_actions)), + alpha_discrete(std::make_shared(5e-2f, 1e-5f, 1e-1f, 1)), actor_optim(std::make_unique( this->actor->parameters(), torch::optim::AdamOptions(actor_learning_rate))), critic_1_optim(std::make_unique( From 8a6e0786f968e98a8e09f1aa1b65160a09981039 Mon Sep 17 00:00:00 2001 From: Ipsedo Date: Thu, 20 Aug 2026 10:02:38 +0200 Subject: [PATCH 63/70] add trained model --- arenai_desktop/src/main.cpp | 2 +- .../ppo-thin-256x128_save_6/actor.pt | Bin 5078949 -> 0 bytes resources/trained_models/ppo_save_32/actor.pt | Bin 0 -> 5072308 bytes 3 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 resources/trained_models/ppo-thin-256x128_save_6/actor.pt create mode 100644 resources/trained_models/ppo_save_32/actor.pt diff --git a/arenai_desktop/src/main.cpp b/arenai_desktop/src/main.cpp index cb792101..046d8899 100644 --- a/arenai_desktop/src/main.cpp +++ b/arenai_desktop/src/main.cpp @@ -88,7 +88,7 @@ int main(const int argc, char **argv) { {.vision_height = parser.get("--vision_height"), .vision_width = parser.get("--vision_width"), .hyper_parameters = hyper_params, - .state_dict_folder = resources_folder / "trained_models" / "ppo-thin-256x128_save_6", + .state_dict_folder = resources_folder / "trained_models" / "ppo_save_32", .cuda = parser.get("--cuda")}); return 0; diff --git a/resources/trained_models/ppo-thin-256x128_save_6/actor.pt b/resources/trained_models/ppo-thin-256x128_save_6/actor.pt deleted file mode 100644 index 5626d57ca612fdc7f6f15287ff2f8b032f061247..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5078949 zcmb4qc{Eqy_pVtKBAKExMuf@`pR+eAlFCpTloFbxQAnYTnTv`f3JoMhisEzj+e9iQ zDovtEg+^3L)AhZ-f9|^XuDgEsto8o=tn;4pzWdqF^KAE7;u0bvLxza_AI~rm6%k+m 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