AutoDRIVE Cooperative MARL is a GitHub-hosted framework combining the AutoDRIVE urban driving simulator with adaptable multi-agent reinforcement learning algorithms. It includes training scripts, environment wrappers, evaluation metrics, and visualization tools to develop and benchmark cooperative driving policies. Users can configure agent observation spaces, reward functions, and training hyperparameters. The repository supports modular extensions, enabling custom task definitions, curriculum learning, and performance tracking for autonomous vehicle coordination research.

This repository provides a complete suite of multi-agent reinforcement learning algorithms—including MADDPG, DDPG, PPO, and more—integrated with standard benchmarks like the Multi-Agent Particle Environment and OpenAI Gym. It features customizable environment wrappers, configurable training scripts, real-time logging, and performance evaluation metrics. Users can easily extend algorithms, adapt to custom tasks, and compare policies across cooperative and adversarial settings with minimal setup.