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.

NavGround Learning provides a comprehensive toolkit for developing and benchmarking reinforcement learning agents in navigation tasks. It supports multi-agent simulation, collision modeling, and customizable sensors and actuators. Users can select from predefined policy templates or implement custom architectures, train with state-of-the-art RL algorithms, and visualize performance metrics. Its integration with OpenAI Gym and Stable Baselines3 simplifies experiment management, while built-in logging and visualization tools allow in-depth analysis of agent behavior and training dynamics.