Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Reinforcement Learning With Tensorflow | 8,174 | 9 months ago | 63 | mit | Python | |||||
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学 | ||||||||||
Easy Rl | 7,643 | 3 months ago | 47 | other | Jupyter Notebook | |||||
强化学习中文教程(蘑菇书),在线阅读地址:https://datawhalechina.github.io/easy-rl/ | ||||||||||
Deep Reinforcement Learning | 4,635 | 5 months ago | 5 | mit | Jupyter Notebook | |||||
Repo for the Deep Reinforcement Learning Nanodegree program | ||||||||||
Reinforcement Learning | 4,090 | 4 years ago | 2 | mit | Jupyter Notebook | |||||
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning | ||||||||||
Cleanrl | 3,947 | 3 months ago | 49 | other | Python | |||||
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG) | ||||||||||
Pytorch A2c Ppo Acktr Gail | 3,450 | 2 years ago | 82 | mit | Python | |||||
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL). | ||||||||||
Elegantrl | 3,229 | 2 | 5 months ago | 5 | January 08, 2022 | 107 | other | Python | ||
Massively Parallel Deep Reinforcement Learning. 🔥 | ||||||||||
Football | 3,177 | 2 | 5 months ago | 27 | January 25, 2022 | 60 | apache-2.0 | Python | ||
Check out the new game server: | ||||||||||
Minimalrl | 2,417 | a year ago | 21 | mit | Python | |||||
Implementations of basic RL algorithms with minimal lines of codes! (pytorch based) | ||||||||||
Rl Baselines3 Zoo | 1,640 | 2 | 3 months ago | 18 | November 17, 2023 | 54 | mit | Python | ||
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. |