#OpenAI Gym

Explore a broad range of sophisticated deep reinforcement learning algorithms in Pytorch, emphasizing continuous action spaces. Efficiently train on CPUs or GPUs and straightforwardly evaluate with OpenAI Gym. This repository includes various model-free and model-based RL algorithms, offering techniques like DDPG, PPO, and soft actor-critic, in addition to experimental methods such as prioritized experience replay. Flexible for extensions, it accommodates environments from classic games to complex robotic tasks.

This project presents a reinforcement learning application using Deep Q-Learning (DQN) to train the Cartpole system. The system requires precise force applications to keep the pole balanced on a frictionless track. Details include specific hyperparameters like a learning rate of 0.001, batch size of 20, and the use of experience replay. Achieving an average reward of 195.0 over 100 trials qualifies as successful, showcasing efficient balance control. Learn more about performance metrics and see example trials of successful execution.

Gym.NET is a C# adaptation of the OpenAI Gym, offering a framework for creating and testing reinforcement learning algorithms. It includes environments like CartPole and LunarLander, with rendering options via WinForm and Avalonia. Future plans involve adding support for more environments and improving compatibility. Installation is straightforward, making it accessible for reinforcement learning exploration in .NET.

Gymnasium provides a consistent API for reinforcement learning, building on OpenAI's Gym by offering diverse environments like Classic Control and Atari. Explore flexible installations and related libraries like CleanRL for comprehensive learning toolkits.

Discover a comprehensive reinforcement learning framework leveraging OpenAI Gym and TensorFlow. This system offers a unified interface, essential RL algorithm implementations, and automated analytics, optimizing algorithm development. Suitable for extensive experimentation, hypothesis testing, and hyperparameter optimization, with settings stored for reproducibility. Evaluate algorithm performance across various environments using the Fitness Matrix. Start developing RL agents with provided components and look forward to future support for PyTorch.

The dmc2gym project offers an OpenAI Gym-compatible wrapper for the DeepMind Control Suite, enabling deterministic simulations with features like random seed initialization, image-based observations, and action space normalization. It is designed for straightforward integration, providing flexibility in AI research setups. The project's installation and usage are simplified through direct pip installation, making it an efficient choice for developers working with AI tools.