🚇 Collective Crossing

A multi-agent reinforcement learning environment for simulating collective behavior in tram boarding/exiting scenarios. This project provides a grid-world environment where multiple agents interact to achieve their goals while sharing some resources together.

🎯 Overview

The CollectiveCrossingEnv simulates a minimal tram boarding scenario where coordination is essential to find the optimal collective behavior:

• Boarding agents start in the platform area and navigate to the tram door
• Exiting agents start inside the tram and navigate to the exit
• Simple collision avoidance prevents agents from occupying the same space, which makes the passing through the tram door a bottleneck and a challenge
• Configurable geometry allows customization of tram size, door position, and environment
• Flexible reward system supports multiple reward strategies (default, simple distance, binary)
• Customizable termination configurable episode termination conditions
• Adaptive truncation flexible episode truncation policies

🚀 Quick Start

from collectivecrossing import CollectiveCrossingEnv
from collectivecrossing.configs import CollectiveCrossingConfig
from collectivecrossing.reward_configs import DefaultRewardConfig
from collectivecrossing.terminated_configs import AllAtDestinationTerminatedConfig
from collectivecrossing.truncated_configs import MaxStepsTruncatedConfig
from collectivecrossing.observation_configs import DefaultObservationConfig

# Create environment with configurable systems
reward_config = DefaultRewardConfig(
    boarding_destination_reward=15.0,
    tram_door_reward=10.0,
    tram_area_reward=5.0,
    distance_penalty_factor=0.1
)

terminated_config = AllAtDestinationTerminatedConfig()
truncated_config = MaxStepsTruncatedConfig(max_steps=100)
observation_config = DefaultObservationConfig()

config = CollectiveCrossingConfig(
    width=12, height=8, division_y=4,
    tram_door_left=5, tram_door_right=6, tram_length=10,
    num_boarding_agents=5, num_exiting_agents=3,
    render_mode="rgb_array",
    reward_config=reward_config,
    terminated_config=terminated_config,
    truncated_config=truncated_config,
    observation_config=observation_config
)

env = CollectiveCrossingEnv(config=config)
observations, infos = env.reset(seed=42)

🎮 Key Features

• Multi-agent simulation with boarding and exiting agents
• Collision avoidance prevents agents from overlapping
• Configurable geometry customizable tram and door positions
• Ray RLlib compatible uses MultiAgentEnv API
• Multiple rendering modes ASCII and RGB visualization
• Type-safe configuration using Pydantic v2
• Flexible reward system multiple reward strategies with custom configurations
• Customizable termination configurable episode ending conditions
• Adaptive truncation flexible episode timeout policies
• Configurable observations customizable observation functions and spaces

📚 Documentation

🌐 Live Documentation - Complete documentation site

• Installation Guide - Detailed setup instructions
• Usage Guide - Complete usage examples and configuration
• Baselines - Baseline policies and demo scripts
• Development Guide - Testing, contributing, and development
• Features Overview - Comprehensive feature descriptions
• Local Deployment - Simple deployment guide

🛠️ Installation

# Clone and install
git clone https://github.com/nima-siboni/collectivecrossing.git
cd collectivecrossing
uv sync

See Installation Guide for detailed instructions.

🚀 Quick Deploy

# Deploy documentation to GitHub Pages
./scripts/docs.sh deploy

See Local Deployment Guide for details.

🧪 Testing

# Run tests
uv run pytest

# Run with coverage
uv run pytest --cov=collectivecrossing

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests
5. Submit a pull request

See Development Guide for detailed contribution guidelines.

📄 License

This project is licensed under the Apache License 2.0.

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Happy simulating! 🚇✨