#RL algorithms

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.

Tensorforce, an open-source library built on TensorFlow, provides a modular architecture for deep reinforcement learning, ideal for research and practical applications. It features a flexible, component-based structure that decouples algorithms from environments, enhancing versatility and user accessibility. Supporting a variety of network architectures, policy distributions, and optimization strategies, Tensorforce facilitates the development of models such as DQN and PPO. The library includes practical example configurations and thorough documentation, although it is important to note that the project is no longer actively maintained.

Stable Baselines3 provides reliable reinforcement learning implementations in PyTorch, facilitating replication and refinement in research and industry. It features advanced RL methods, customizable setups, and integrations with Tensorboard and Hugging Face. Suitable for those with reinforcement learning knowledge, it supports both beginners and experts with detailed documentation and community involvement. Ideal for boosting RL projects and experimentation.

SB3-Contrib provides experimental reinforcement learning algorithms and utilities that extend stable-baselines3, featuring advanced implementations such as Augmented Random Search, Quantile Regression DQN, and PPO with recurrent policy. Aimed at researchers, it includes niche tools that support a broad range of RL research and applications, facilitating the integration and exploration of innovative enhancements in reinforcement learning.

This framework facilitates training and evaluation of RL agents with Stable Baselines3, offering scripts for hyperparameter tuning, performance evaluation, and utilizing pre-trained models. It supports benchmarking across algorithms with a comprehensive set of tuned settings for diverse environments, providing clear documentation for setup, training, and integration with tools such as Weights & Biases. Suitable for all levels of RL practitioners.