tkm-agentsim 0.1.0

Multi-agent simulation environment with scenarios, analysis, and visualization

AgentSim

Multi-agent simulation framework for studying agent behaviors in grid-based environments. Supports reactive, deliberative (BDI), and reinforcement-learning agents with built-in scenarios, metrics, and ASCII visualization.

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Features

• Three agent architectures — Reactive (condition-action rules), Deliberative (BDI goals/beliefs/plans), and Q-learning agents in a single unified interface.
• Grid environment — 2D grid with configurable walls, food resources, and multi-agent support; extend via BaseEnvironment.
• Turnkey scenarios — ForagingScenario (multi-agent food collection) and PursuitScenario (predator-prey) runnable in one call.
• Episode orchestration — Simulation drives agent-environment loops across multiple episodes, tracking per-agent rewards and step counts.
• Metrics and analysis — compute_metrics and compute_trajectory_stats aggregate episode results into structured summaries.
• ASCII visualization — render_grid_ascii and simulation_report produce plain-text grid snapshots and run reports with no GUI dependency.

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Quick Start

pip install agentsim

from agentsim import (
    GridEnvironment,
    LearningAgent,
    Simulation,
    SimulationConfig,
    compute_metrics,
    simulation_report,
)

# Build environment and agent
env = GridEnvironment(width=10, height=10, n_food=15, n_walls=8)
agent = LearningAgent("learner", position=(0, 0))
env.add_agent(agent)

# Run 20 episodes
cfg = SimulationConfig(max_steps=200, n_episodes=20)
sim = Simulation(env, [agent], config=cfg)
results = sim.run()

# Analyse and display
metrics = compute_metrics(results, [agent])
print(simulation_report(results, [agent]))
print(sim.summary())

Use a built-in scenario instead:

from agentsim import ForagingScenario, make_forager

scenario = ForagingScenario(grid_size=12, n_food=20)
agents = [make_forager(f"agent_{i}", position=(i, 0)) for i in range(3)]
result = scenario.run(agents)
print(f"Collected {result.total_collected} food in {result.steps} steps "
      f"(efficiency {result.efficiency:.2f})")

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Architecture

agentsim/
├── agents/
│   ├── base.py          # BaseAgent, AgentState — abstract interface
│   ├── reactive.py      # ReactiveAgent — condition-action rules
│   ├── deliberative.py  # DeliberativeAgent — BDI (goals, beliefs, plans)
│   └── learning.py      # LearningAgent — Q-learning, epsilon-greedy
├── environment/
│   ├── base.py          # BaseEnvironment — reset/step/render contract
│   └── grid.py          # GridEnvironment — 2D grid, walls, food
├── scenarios/
│   ├── foraging.py      # ForagingScenario, make_forager()
│   └── pursuit.py       # PursuitScenario, make_predator(), make_prey()
├── simulation.py        # Simulation, SimulationConfig, EpisodeResult
├── analysis.py          # compute_metrics, compute_trajectory_stats
└── viz.py               # render_grid_ascii, simulation_report

Data flow per episode:

1. Simulation.run_episode() calls env.reset() → returns initial observations per agent.
2. Each step: agent.step(obs) → action → env.step(agent_id, action) → (new_obs, reward, done).
3. agent.receive_reward(reward) updates internal state; loop continues until done or max_steps.
4. EpisodeResult collected; compute_metrics() aggregates across episodes.

Extension points:

• New agent type: subclass BaseAgent, implement perceive() and decide().
• New environment: subclass BaseEnvironment, implement reset(), step(), render().
• New scenario: compose agents + environment setup and delegate to Simulation.

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Development

git clone https://github.com/techknowmad/agent-sim.git
cd agent-sim
pip install -e ".[dev]"
pytest -v
ruff check .

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Contributing

See CONTRIBUTING.md for branch, test, and PR conventions.

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License

MIT — see LICENSE.

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Built by TechKnowMad Labs