AutoDRIVE Cooperative MARL is an open-source framework designed to train and deploy cooperative multi-agent reinforcement learning (MARL) policies for autonomous driving tasks. It integrates with realistic simulators to model traffic scenarios like intersections, highway platooning, and merging. The framework implements centralized training with decentralized execution, enabling vehicles to learn shared policies that maximize overall traffic efficiency and safety. Users can configure environment parameters, choose from baseline MARL algorithms, visualize training progress, and benchmark agent coordination performance.

DeepMind MAS Environment is a Python library that provides a standardized interface for building and simulating multi-agent reinforcement learning tasks. It allows users to configure number of agents, define observation and action spaces, and customize reward structures. The framework supports agent-to-agent communication channels, performance logging, and rendering capabilities. Researchers can seamlessly integrate DeepMind MAS Environment with popular RL libraries such as TensorFlow and PyTorch to benchmark new algorithms, test communication protocols, and analyze both discrete and continuous control domains.

Multi-Agent Miners offers a grid-world environment where multiple autonomous miner agents navigate, dig, and collect resources while interacting with each other. It supports configurable map sizes, agent counts, and reward structures, allowing users to create competitive or cooperative scenarios. The framework integrates with popular RL libraries via PettingZoo, providing standardized APIs for reset, step, and render functions. Visualization modes and logging support help analyze behaviors and outcomes, making it ideal for research, education, and algorithm benchmarking in multi-agent reinforcement learning.