Tutorial4RL: Tutorial for Reinforcement Learning. 强化学习入门教程.
Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".
PyTorch implementation of "Asynchronous advantage actor-critic"
PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3.
强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/
Implementation of all RL algorithms in a simpler way
RL-Odyssey is a research framework for continuous control that implements state-of-the-art RL algorithms (SAC, TD3, PPO, etc.) with clean experiment scripts and interactive notebooks.
PyTorch implementation of A3C (Asynchronous Advantage Actor Critic)
ScaleRL is a simple and scalable distributed reinforcement learning framework based on Python and PyTorch
PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) with parallel learning
A continuous action space version of A3C LSTM in pytorch plus A3G design
Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
A collection of my implemented advanced & complex RL agents for games like Soccer, Street Fighter, Mortal Kombat, Rubik's Cube, Vizdoom, Montezuma, Kungfu-master, Super-Mario-bros, HalfCheetah and more by implementing advanced DRL concepts like decision transformers, RND, MARL, A3C, ICM & sample_factory. To see my other rl agents please visit
RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code
Projects and Models built in Python leveraging PyTorch, implementing Reinforcement Learning algorithms for reward-based tasks.
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