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.

Dino Reinforcement Learning offers a comprehensive toolkit for training an AI agent to play the Chrome dinosaur game via reinforcement learning. By integrating with a headless Chrome instance through Selenium, it captures real-time game frames and processes them into state representations optimized for deep Q-network inputs. The framework includes modules for replay memory, epsilon-greedy exploration, convolutional neural network models, and training loops with customizable hyperparameters. Users can monitor training progress via console logs and save checkpoints for later evaluation. Post-training, the agent can be deployed to play live games autonomously or benchmarked against different model architectures. The modular design allows easy substitution of RL algorithms, making it a flexible platform for experimentation.