MUD Arena

An agent simulation arena using MUD (Multi-User Dungeon) mechanics for the OpenConstruct ecosystem. Agents navigate graph-structured rooms, manage inventories, parse adventure-game commands, and compete in evolutionary tournaments — with GPU-accelerated simulation, LLM-driven scenario generation, and real-time WebSocket observation.

Why It Matters

MUD Arena is a gym environment for AI agents: it provides a text-adventure world with grounded mechanics (spatial navigation, resource management, combat) that are:

• Richer than GridWorld — graph topology, items, NPCs, hazards, multi-agent interaction.
• More structured than free-form LLM chat — discrete state, rule-based physics, measurable outcomes.
• Evolution-ready — built-in genetic algorithm engine for breeding agent decision scripts across generations.
• Observable — real-time WebSocket, Telnet, and HTTP interfaces for human supervision.

The arena serves as a testbed for studying agent generalization, emergent cooperation, and the co-evolution of strategies and environments.

How It Works

Core Simulation Loop

For each tick:
  1. For each agent A:
     a. perceive(A) → perception dict {room, exits, items, npcs, inventory}
     b. decide(A, perception) → Command{verb, target}
     c. act(A, command) → mutate world state, emit Event
  2. Resolve combat, apply hazards, update scores
  3. Publish world snapshot to watchers (WebSocket/Telnet/HTTP)

Spatial Model

The world is a RoomGraph — a directed graph of Room nodes connected by labeled exits:

Room {
    id, name, description,
    exits: {direction → room_id},
    items: [item names on ground],
    npcs: [present NPCs],
    metadata: {lighting, hazards, …}
}

Command Parsing

The command parser supports MUD-standard verbs:

Verb	Aliases	Example
GO	move, walk, run, head	go north
LOOK	l	look
EXAMINE	x, inspect	examine crystal
TAKE	get, pick up, grab	take key
DROP	—	drop torch
USE	—	use key with door
TALK	—	talk to guard

Evolution Engine

The genetic algorithm operates on agent scripts (rule lists in a custom DSL):

1. Initialize — random population of N scripts
2. Evaluate — run each script on K scenarios, score = survival time / objectives
3. Select — tournament selection of elites
4. Crossover — single-point recombination of parent rule lists
5. Mutate — per-gene mutation at rate μ
6. Replace — swap worst performers with offspring
7. Repeat for G generations

Optional GPU acceleration via PyTorch for batch evaluation. LLM hooks for:

• Scenario generation — GPT generates thematically rich environments
• Strategy review — LLM analyzes top scripts and suggests improvements

Complexity

Operation	Time
Agent perception	O(1) per room lookup
Command parsing	O(k) where k = tokens
Simulation tick	O(A) where A = agents
Evolution generation	O(N · K · S) where N = pop, K = scenarios, S = avg ticks
Script crossover	O(min(len_a, len_b))
Script mutation	O(len)

Quick Start

# Install
pip install -e ".[server,evolution]"

# Run server
python src/server.py

# Run evolution
python src/evolve.py --generations 100 --population 200 --scenarios 20

# Generate scenarios
python src/scenario_generator.py --random --rooms 12 --difficulty 4

# Compile scripts
python src/script_compiler.py --dsl "attack;move north;take key"

API

Core Module (mud_arena)

Module	Key Types
rooms.py	Room, RoomGraph — spatial world model
agent.py	Agent — perceive/decide/act loop with pluggable DecisionFn
commands.py	Command, Verb, parse_command()
inventory.py	Item, Inventory — capacity-limited item containers
events.py	Event, EventBus — pub/sub for world events

Simulation Modules

Module	Function
server.py	WebSocket (7779), Telnet (7778), HTTP (7780) observation server
evolve.py	Genetic algorithm engine with GPU acceleration
scenario_generator.py	Random and LLM-driven scenario creation
script_compiler.py	DSL ↔ binary compilation, mutation, crossover
tolerance.py	Simulation-vs-reality tolerance tracking
dashboard.py	HTML dashboard generation for evolution results

Architecture Notes

The arena is polyglot: Python core (src/mud_arena/), CUDA kernels (src/mud_arena.cu), Zig bindings (src/mud_arena.zig), WASM target (src/wasm_mud.c), and web interface (src/mud_arena.html).

The γ + η = C ternary classification: each agent action is either (γ) exploratory (navigating, searching, gathering — low-risk information gain) or (η) exploitative (combat, resource consumption, goal completion — high-risk reward). The balance γ/(γ+η) is the exploration-exploitation ratio, a fundamental tradeoff in reinforcement learning.

References

1. Bartle, R. (2003). Designing Virtual Worlds. New Riders. — MUD design philosophy.
2. Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
3. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. MIT Press. — Genetic algorithms.
4. Schmidhuber, J. (2015). "Deep learning in neural networks: An overview." Neural Networks, 61, 85–117.
5. OpenAI (2024). "Emergent tool use from multi-agent autocurricula." arXiv preprint.

License

MIT