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Comprehensive vectorized environments Tools for Every Need

Get access to vectorized environments solutions that address multiple requirements. One-stop resources for streamlined workflows.

vectorized environments

  • Fast Reinforcement Learning

    A high-performance Python framework delivering fast, modular reinforcement learning algorithms with multi-environment support.
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    What is Fast Reinforcement Learning?
    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.
    Fast Reinforcement Learning Core Features
    • Vectorized environment manager for parallel simulation
    • Implementations of PPO, A2C, DDPG and SAC
    • Configurable policy and value networks
    • GPU acceleration support via PyTorch
    • Modular training loop and callback system
    • Compatibility with OpenAI Gym

  • PyGame Learning Environment

    PyGame Learning Environment provides a collection of Pygame-based RL environments for training and evaluating AI agents in classic games.
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    What is PyGame Learning Environment?
    PyGame Learning Environment (PLE) is an open-source Python framework designed to simplify the development, testing, and benchmarking of reinforcement learning agents within custom game scenarios. It provides a collection of lightweight Pygame-based games with built-in support for agent observations, discrete and continuous action spaces, reward shaping, and environment rendering. PLE features an easy-to-use API compatible with OpenAI Gym wrappers, enabling seamless integration with popular RL libraries such as Stable Baselines and TensorForce. Researchers and developers can customize game parameters, implement new games, and leverage vectorized environments for accelerated training. With active community contributions and extensive documentation, PLE serves as a versatile platform for academic research, education, and real-world RL application prototyping.
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