Fast Reinforcement Learning is a specialized Python framework designed to accelerate the development and execution of reinforcement learning agents. It offers out-of-the-box support for popular algorithms such as PPO, A2C, DDPG and SAC, combined with high-throughput vectorized environment management. Users can easily configure policy networks, customize training loops and leverage GPU acceleration for large-scale experiments. The library’s modular design ensures seamless integration with OpenAI Gym environments, enabling researchers and practitioners to prototype, benchmark and deploy agents across a variety of control, game and simulation tasks.

Shumai is a powerful tensor library designed for JavaScript and TypeScript, created by Facebook Research (FAIR). The library stands out for its high performance, network connectivity, and differentiable capabilities. Built using Bun and Flashlight, it enables developers to seamlessly integrate deep learning and machine learning functionalities into web applications. It supports features such as GPU computation, making it ideal for complex scientific computations and model training. Shumai is aimed at providing a robust environment for developing advanced machine learning models in a TypeScript ecosystem.