multiagent-env provides researchers and developers with a flexible Python framework to simulate and benchmark multi-agent reinforcement learning tasks. It offers a gym-style interface for creating and managing cooperative, competitive, and mixed scenarios, complete with customizable reward structures, observation spaces, and rendering options. The repository includes several example environments and supports easy integration with popular RL libraries.
multiagent-env provides researchers and developers with a flexible Python framework to simulate and benchmark multi-agent reinforcement learning tasks. It offers a gym-style interface for creating and managing cooperative, competitive, and mixed scenarios, complete with customizable reward structures, observation spaces, and rendering options. The repository includes several example environments and supports easy integration with popular RL libraries.
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.
Who will use multiagent-env?
Multi-agent RL researchers
Machine learning students
Academic educators
RL algorithm developers
Open-source contributors
How to use the multiagent-env?
Step1: Clone the repository from GitHub or install via pip.
Step2: Import the environment module in your Python script.
Step3: Instantiate a scenario by name or custom configuration.
Step4: Reset the environment and run simulation steps to collect observations, actions, and rewards.
Step5: Integrate with RL training loop for policy updates.
Step6: Render environment or log metrics for analysis.