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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
| Date | Stars |
|---|---|
| 2026-07-24 | 3417 |
| 2026-07-25 | 3419 |
| 2026-07-28 | 3419 |
| 2026-07-30 | 3419 |
| 2026-07-31 | 3424 |
| 2026-08-04 | 3425 |
| 2026-08-05 | 3428 |
| 2026-08-06 | 3430 |
Today
+2 stars today
This week
+11 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.32%/day
<div align=center> <img src="https://github.com/XinJingHao/RL-Algorithms-by-Pytorch/blob/main/RL_PYTORCH.png" width=500 /> </div> <div align=center> Clean, Robust, and Unified PyTorch implementation of popular DRL Algorithms </div> <div align=center> <img src="https://img.shields.io/badge/Python-blue" /> <img src="https://img.shields.io/badge/DRL-blueviolet" /> <a href="https://pytorch.org/"><img src="https://img.shields.io/badge/Pytorch-ff69b4" /></a> <a href="https://deepwiki.com/XinJingHao/DRL-Pytorch"><img src="https://img.shields.io/badge/DeepWiki-FF6347" /></a> </div> <br/> <br/> ## 0.Star History <div align="left"> <img width="70%" height="auto" src="https://api.star-history.com/svg?repos=XinJingHao/Deep-Reinforcement-Learning-Algorithms-with-Pytorch&type=Date"> </div> <br/> ## 1.Dependencies This repository uses the following python dependencies unless explicitly stated: ```python gymnasium==0.29.1 numpy==1.26.1 pytorch==2.1.0 python==3.11.5 ``` <br/> ## 2.How to use my code Enter the folder of the algorithm that you want to use, and run the **main.py** to train from scratch: ```bash python main.py ``` For more details, please check the **README.md** file in the corresponding algorithm folder. <br/> ## 3. Separate links of the code + [1.Q-learning](https://github.com/XinJingHao/Q-learning) + [2.1Duel Double DQN](https://github.com/XinJingHao/Duel-Double-DQN-Pytorch) + [2.2Noisy Duel DDQN on Atari Game](https://github.com/XinJingHao/Noisy-Duel-DDQN-Atari-Pytorch) + [2.3Prioritized Experience Replay(PER) DQN/DDQN](https://github.com/XinJingHao/Prioritized-DQN-DDQN-Pytorch) + [2.4Categorical DQN (C51)](https://github.com/XinJingHao/C51-Categorical-DQN-Pytorch) + [2.5NoisyNet DQN](https://github.com/XinJingHao/NoisyNet-DQN-Pytorch) + [3.1Proximal Policy Optimization(PPO) for Discrete Action Space](https://github.com/XinJingHao/PPO-Discrete-Pytorch) + [3.2Proximal Policy Optimization(PPO) for Continuous Action Space](https://github.com/XinJingHao/PPO-Continuous-Pytorch) + [4.1Deep Deternimistic Policy Gradient(DDPG)](https://github.com/XinJingHao/DDPG-Pytorch) + [4.2Twin Delayed Deep Deterministic Policy Gradient(TD3)](https://github.com/XinJingHao/TD3-Pytorch) + [5.1Soft Actor Critic(SAC) for Discrete Action Space](https://github.com/XinJingHao/SAC-Discrete-Pytorch) + [5.2Soft Actor Critic(SAC) for Continuous Action Space](https://github.com/XinJingHao/SAC-Continuous-Pytorch) + [6.Actor-Sharer-Learner(ASL)](https://github.com/XinJingHao/Actor-Sharer-Learner) <br/> ## 4. Recommended Resources for DRL ### 4.1 Simulation Environments: + [gym](https://www.gymlibrary.dev/) and [gymnasium](https://gymnasium.farama.org/) (Lightweight & Standard Env for DRL; Easy to start; Slow): <div align="left"> <img width="60%" height="auto" src="https://github.com/XinJingHao/Images/blob/main/Env_images/gym.gif"> </div> <br/> + [Isaac Sim](https://developer.nvidia.com/isaac/sim#isaac-lab) (NVIDIA’s physics simulation environment; GPU accelerated; Superfast): <div align="left"> <img width="60%" height="auto" src="https://github.com/XinJingHao/Images/blob/main/Env_images/IsaacGym.gif"> </div> <br/> + [Sparrow](https://github.com/XinJingHao/Sparrow-V2) (Light Weight Simulator for Mobile Robot; DRL friendly): <div align="left"> <img width="62%" height="auto" src="https://github.com/XinJingHao/Images/blob/main/Sparrow_V1/render.gif"> </div> <p align="left"> <img src="https://github.com/XinJingHao/Images/blob/main/Sparrow_V2/case_v2.gif" width="10%" height="auto" /> <img src="https://github.com/XinJingHao/Images/blob/main/Sparrow_V2/case2.gif" width="10%" height="auto" /> <img src="https://github.com/XinJingHao/Images/blob/main/Sparrow_V2/play.gif" width="10%" height="auto" /> <img src="https://github.com/XinJingHao/Images/blob/main/Sparrow_V3/N1.gif" width="10%" height="auto" /> <img src="https://github.com/XinJingHao/Images/blob/main/Sparrow_V3/N3.gif" width="10%" height="auto" /> <img src="https://github.com/
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:63fb7e81c1abcc19, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning