DEf-MARL (Decentralized Execution Framework for Multi-Agent Reinforcement Learning) provides a robust infrastructure to execute and train cooperative agents without centralized controllers. It leverages peer-to-peer communication protocols to share policies and observations among agents, enabling coordination through local interactions. The framework integrates seamlessly with common RL toolkits like PyTorch and TensorFlow, offering customizable environment wrappers, distributed rollout collection, and gradient synchronization modules. Users can define agent-specific observation spaces, reward functions, and communication topologies. DEf-MARL supports dynamic agent addition and removal at runtime, fault-tolerant execution by replicating critical state across nodes, and adaptive communication scheduling to balance exploration and exploitation. It accelerates training by parallelizing environment simulations and reducing central bottlenecks, making it suitable for large-scale MARL research and industrial simulations.

Mean-Field MARL provides a robust Python framework for implementing and evaluating mean-field multi-agent reinforcement learning algorithms. It approximates large-scale agent interactions by modeling the average effect of neighboring agents via mean-field Q-learning. The library includes environment wrappers, agent policy modules, training loops, and evaluation metrics, enabling scalable training across hundreds of agents. Built on PyTorch for GPU acceleration, it supports customizable environments like Particle World and Gridworld. Modular design allows easy extension with new algorithms, while built-in logging and Matplotlib-based visualization tools track rewards, loss curves, and mean-field distributions. Example scripts and documentation guide users through setup, experiment configuration, and result analysis, making it ideal for both research and prototyping of large-scale multi-agent systems.

The MARL Simulator is designed to facilitate efficient and scalable development of multi-agent reinforcement learning (MARL) algorithms. Leveraging PyTorch's distributed backend, it allows users to run parallel training across multiple GPUs or nodes, significantly reducing experiment runtime. The simulator offers a modular environment interface that supports standard benchmark scenarios—such as cooperative navigation, predator-prey, and grid world—as well as user-defined custom environments. Agents can utilize various communication protocols to coordinate actions, share observations, and synchronize rewards. Configurable reward and observation spaces enable fine-grained control over training dynamics, while built-in logging and visualization tools provide real-time insights into performance metrics.

Multi-Agent Reinforcement Learning by alaamoheb is a comprehensive open-source library designed to facilitate the development, training, and evaluation of multiple agents acting in shared environments. It includes modular implementations of value-based and policy-based algorithms such as DQN, PPO, MADDPG, and more. The repository supports integration with OpenAI Gym, Unity ML-Agents, and the StarCraft Multi-Agent Challenge, allowing users to experiment in both research and real-world inspired scenarios. With configurable YAML-based experiment setups, logging utilities, and visualization tools, practitioners can monitor learning curves, tune hyperparameters, and compare different algorithms. This framework accelerates experimentation in cooperative, competitive, and mixed multi-agent tasks, streamlining reproducible research and benchmarking.

multiagent-env is an open-source Python library designed to simplify the creation and evaluation of multi-agent reinforcement learning environments. Users can define both cooperative and adversarial scenarios by specifying agent count, action and observation spaces, reward functions, and environmental dynamics. It supports real-time visualization, configurable rendering, and easy integration with Python-based RL frameworks such as Stable Baselines and RLlib. The modular design allows rapid prototyping of new scenarios and straightforward benchmarking of multi-agent algorithms.

multiagent_envs delivers a modular set of Python-based environments tailored for multi-agent reinforcement learning research and development. It includes scenarios like cooperative navigation, predator-prey, social dilemmas, and competitive arenas. Each environment lets you define the number of agents, observation features, reward functions, and collision dynamics. The framework integrates seamlessly with popular RL libraries such as Stable Baselines and RLlib, allowing vectorized training loops, parallel execution, and easy logging. Users can extend existing scenarios or create new ones by following a simple API, accelerating experimentation with algorithms like MADDPG, QMIX, and PPO in a consistent, reproducible setup.

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.