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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.
This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch
| Date | Stars |
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| 2026-07-31 | 453 |
| 2026-08-01 | 453 |
| 2026-08-02 | 453 |
| 2026-08-06 | 453 |
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# Deep Reinforcement Learning in Pytorch
<img width="160px" height="22px" href="https://github.com/pytorch/pytorch" src="https://pp.userapi.com/c847120/v847120960/82b4/xGBK9pXAkw8.jpg">
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<td><img src="/assets/r_her.gif?raw=true" width="200"></td>
<td><img src="/assets/goal-3.png?raw=true" width="200"></td>
<td><img src="/assets/virtual-goal.png?raw=true" width="200"></td>
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This repository contains all standard model-free and model-based(coming) RL algorithms in Pytorch. (May also contain some research ideas I am working on currently)
For C++ version of Pytorch-RL : [Pytorch-RL-CPP](https://github.com/navneet-nmk/Pytorch-RL-CPP)
# What is it?
pytorch-rl implements some state-of-the art deep reinforcement learning algorithms in Pytorch, especially those concerned with continuous action spaces. You can train your algorithm efficiently either on CPU or GPU. Furthermore, pytorch-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend pytorch-rl according to your own needs.
TL:DR : pytorch-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms.
# Installation
Install Pytorch-rl from Pypi (recommended):
pip install pytorch-policy
# Dependencies
1. Pytorch
2. Gym (OpenAI)
3. mujoco-py (For the physics simulation and the robotics env in gym)
4. Pybullet (Coming Soon)
5. MPI (Only supported with mpi backend Pytorch installation)
6. Tensorboardx (https://github.com/lanpa/tensorboardX)
# RL algorithms
1. DQN (with Double Q learning)
2. DDPG
3. DDPG with HER (For the OpenAI Fetch Environments)
4. Heirarchical Reinforcement Learning
5. Prioritized Experience Replay + DDPG
6. DDPG with Prioritized Hindsight experience replay (Research)
7. Neural Map with A3C (Coming Soon)
8. Rainbow DQN (Coming Soon)
9. PPO (https://github.com/ikostrikov/pytorch-a2c-ppo-acktr)
10. HER with self attention for goal substitution (Research)
11. A3C (Coming Soon)
12. ACER (Coming Soon)
13. DARLA
14. TDM
15. World Models
16. Soft Actor-Critic
17. Empowerment driven Exploration (Tensorflow implementation : https://github.com/navneet-nmk/Empowerment-driven-Exploration)
# Environments
1. Breakout
2. Pong (coming soon)
3. Hand Manipulation Robotic Task
4. Fetch-Reach Robotic Task
5. Hand-Reach Robotic Task
6. Block Manipulation Robotic Task
7. Montezuma's Revenge (Current Research)
8. Pitfall
9. Gravitar
10. CarRacing
11. Super Mario Bros (Follow instructions to install gym-retro https://github.com/openai/retro)
12. OpenSim Prosthetics Nips Challenge (https://www.crowdai.org/challenges/nips-2018-ai-for-prosthetics-challenge)
# Environment Modelling (For exploration and domain adaptation)
Multiple GAN training tricks have been used because of the instability in training the generators and discriminators.
Please refer to https://github.com/soumith/ganhacks for more information.
Even after using the tricks, it was really hard to train a GAN to convergence.
However, after using Spectral Normalization (https://arxiv.org/abs/1802.05957) the infogan was trained to convergence.
For image to image translation tasks with GANs and for VAEs in general, training with Skip Connection really helps the training.
1. beta-VAE
2. InfoGAN
3. CVAE-GAN
4. Flow based generative models (Research)
5. SAGAN
6. Sequential Attend, Infer, Repeat
7. Curiosity driven exploration
6. Parameter Space Noise for Exploration
7. Noisy Network
# References
1. Playing Atari with Deep Reinforcement Learning, Mnih et al., 2013
2. Human-level control through deep reinforcement learning, Mnih et al., 2015
3. Deep Reinforcement Learning with Double Q-learning, van Hasselt et al., 2015
4. Continuous control with deep reinforcement learning, Lillicrap et al., 2015
5. CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training, Bao et al., 2017
6. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational FramewExcerpt of 5,035 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:26247ab986e0a30b, topic:reinforcement-learning, topic:deep-reinforcement-learning, topic:openai-gym
matched fp:26247ab986e0a30b, topic:deep-learning, topic:pytorch