MGym is a specialized framework for crafting and managing multi-agent reinforcement learning (MARL) environments in Python. It enables users to define complex scenarios with multiple agents, each having customizable observation and action spaces, reward functions, and interaction rules. MGym supports both synchronous and asynchronous execution modes, providing parallel and turn-based agent simulations. Built with a familiar Gym-like API, MGym seamlessly integrates with popular RL libraries such as Stable Baselines, RLlib, and PyTorch. It includes utility modules for environment benchmarking, result visualization, and performance analytics, facilitating systematic evaluation of MARL algorithms. Its modular architecture allows rapid prototyping of cooperative, competitive, or mixed-agent tasks, empowering researchers and developers to accelerate MARL experimentation and research.

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-Prediction-Reward is a research-oriented framework that integrates prediction models and reward distribution mechanisms for multi-agent reinforcement learning. It includes environment wrappers, neural modules for forecasting peer actions, and customizable reward routing logic that adapts to agent performance. The repository provides configuration files, example scripts, and evaluation dashboards to run experiments on cooperative tasks. Users can extend the code to test novel reward functions, integrate new environments, and benchmark against established multi-agent RL algorithms.

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.

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.