Meta PyTorch OpenEnv Hackathon - Round 1 Submission

Project: Autonomous Traffic Control Environment

Project Structure

• env.py: The custom Gymnasium environment implementation (AutonomousTrafficControlEnv).
• train.py: A script to train a baseline agent using stable-baselines3 (PPO).
• grader.py: Evaluator script to test and grade agent performance based on emergency wait times.
• Dockerfile: Container configuration required for OpenEnv compatibility.
• requirements.txt: Python package dependencies.

Setup Instructions

Local Development

1. Clone or download this repository.
2. Install the required dependencies:
   pip install -r requirements.txt
3. Test the environment and run the evaluation script:
   python grader.py
4. Train the PPO agent:
   python train.py

Docker Execution (Evaluation)

Features

• State Space: Tracks normal and emergency vehicle queues for 4 directions (N, S, E, W).
• Action Space: Controls 4 distinct green light phases.
• Reward Logic:
  • Penalty for global waiting times.
  • Severe penalty for delayed emergency vehicles to enforce prioritization.
• API strictly adheres to gymnasium formatting to integrate seamlessly with standard RL libraries and OpenEnv containers.