Mava is a JAX-based open-source library for developing, training, and evaluating multi-agent reinforcement learning systems. It offers pre-built implementations of cooperative and competitive algorithms such as MAPPO and MADDPG, along with configurable training loops that support single-node and distributed workflows. Researchers can import environments from PettingZoo or define custom environments, then use Mava’s modular components for policy optimization, replay buffer management, and metric logging. The framework’s flexible architecture allows seamless integration of new algorithms, custom observation spaces, and reward structures. By leveraging JAX’s auto-vectorization and hardware acceleration capabilities, Mava ensures efficient large-scale experiments and reproducible benchmarking across various multi-agent scenarios.

TensorFlow provides a comprehensive ecosystem for developing machine learning models, supporting tasks such as data processing, model training, and deployment. With its flexibility and scalability, TensorFlow allows for the building of complex architectures like neural networks, facilitating applications in fields such as computer vision, natural language processing, and robotics.