| 1 |
https://github.com/openai/baselines |
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms |
| 2 |
https://github.com/hill-a/stable-baselines |
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms |
| 3 |
https://github.com/openai/spinningup |
An educational resource to help anyone learn deep reinforcement learning. |
| 4 |
https://github.com/google/dopamine |
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. |
| 5 |
https://github.com/tensorflow/agents |
TF-Agents is a library for Reinforcement Learning in TensorFlow |
| 6 |
https://github.com/deepmind/trfl |
TensorFlow Reinforcement Learning |
| 7 |
https://github.com/facebookresearch/Horizon |
A platform for Applied Reinforcement Learning (Applied RL) |
| 8 |
https://github.com/facebookresearch/ELF |
An End-To-End, Lightweight and Flexible Platform for Game Research |
| 9 |
https://github.com/NervanaSystems/coach |
Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms |
| 10 |
https://github.com/ray-project/ray/tree/master/python/ray/rllib |
A fast and simple framework for building and running distributed applications. |
| 11 |
https://github.com/keras-rl/keras-rl |
Deep Reinforcement Learning for Keras. |
| 12 |
https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail |
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). |
| 13 |
https://github.com/Kaixhin/Rainbow |
Rainbow: Combining Improvements in Deep Reinforcement Learning |
| 14 |
https://github.com/MillionIntegrals/vel |
Velocity in deep-learning research |
| 15 |
https://github.com/tensorforce/tensorforce |
Tensorforce: A TensorFlow library for applied reinforcement learning |
| 16 |
https://github.com/kengz/SLM-Lab |
Modular Deep Reinforcement Learning framework in PyTorch. |
| 17 |
https://github.com/rlworkgroup/garage |
A framework for reproducible reinforcement learning research |
| 18 |
https://github.com/catalyst-team/catalyst |
Reproducible and fast DL & RL. |
| 19 |
https://github.com/higgsfield/RL-Adventure |
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL |
| 20 |
https://github.com/qfettes/DeepRL-Tutorials |
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch |
| 21 |
https://github.com/openai/gym |
A toolkit for developing and comparing reinforcement learning algorithms. |
| 22 |
https://github.com/deepmind/lab |
A customisable 3D platform for agent-based AI research |
| 23 |
https://github.com/Microsoft/malmo |
Project Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment. — For installation instruct |
| 24 |
https://github.com/openai/retro |
Retro Games in Gym |
| 25 |
https://github.com/deepmind/dm_control |
The DeepMind Control Suite and Package |
| 26 |
https://github.com/openai/neural-mmo |
Neural MMO – A Massively Multiagent Game Environment |
| 27 |
https://github.com/openai/gym |
Gym @ OpenAI |
| 28 |
https://github.com/deepmind/lab |
Lab @ DeepMind |
| 29 |
https://github.com/Microsoft/malmo |
Project Malmo @ Microsoft |
| 30 |
https://github.com/openai/retro |
Retro @ OpenAI |
| 31 |
https://github.com/deepmind/dm_control |
Control Suite @ DeepMind |
| 32 |
https://github.com/openai/neural-mmo |
Neural MMO @ OpenAI |
| 33 |
https://github.com/openai/baselines |
Tensorflow Maintained by OpenAI |
| 34 |
https://github.com/hill-a/stable-baselines |
Tensorflow Maintained by Antonin Raffin, Ashley Hill |
| 35 |
https://github.com/catalyst-team/catalyst |
PyTorch Maintained by Sergey Kolesnikov |
| 36 |
https://github.com/ray-project/ray/tree/master/python/ray/rllib |
Tensorflow Maintained by Ray Team |
| 37 |
https://github.com/tensorflow/agents |
Tensorflow Maintained by Google |
| 38 |
https://github.com/facebookresearch/Horizon |
PyTorch Maintained by Facebook |
| 39 |
https://github.com/NervanaSystems/coach |
Tensorflow Maintained by Intel |
| 40 |
https://github.com/rlworkgroup/garage |
Tensorflow Maintained by community |
| 41 |
https://github.com/kengz/SLM-Lab |
PyTorch Maintained by Wah Loon Keng, Laura Graesser |
| 42 |
https://github.com/google/dopamine |
Tensorflow Maintained by Google |
| 43 |
https://github.com/openai/spinningup |
Tensorflow Maintained by OpenAI |
| 44 |
https://github.com/deepmind/trfl |
Tensorflow Maintained by DeepMind |
| 45 |
https://github.com/deepmind/scalable_agent |
Tensorflow Maintained by DeepMind |
| 46 |
https://github.com/facebookresearch/ELF |
PyTorch Maintained by Facebook |
| 47 |
https://github.com/keras-rl/keras-rl |
Tensorflow Maintained by Matthias Plappert |
| 48 |
https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail |
PyTorch Maintained by Ilya Kostrikov |
| 49 |
https://github.com/Kaixhin/Rainbow |
PyTorch Maintained by Kai Arulkumaran |
| 50 |
https://github.com/MillionIntegrals/vel |
PyTorch Maintained by Jerry (?) |
| 51 |
https://github.com/Khrylx/PyTorch-RL |
PyTorch |
| 52 |
https://github.com/tensorforce/tensorforce |
Tensorflow |
| 53 |
https://github.com/higgsfield/RL-Adventure |
PyTorch |
| 54 |
https://github.com/qfettes/DeepRL-Tutorials |
PyTorch |
| 55 |
https://github.com/SurrealAI/surreal |
TorchX |
| 56 |
https://github.com/zuoxingdong/lagom |
PyTorch |
| 57 |
https://github.com/dennybritz/reinforcement-learning |
Tensorflow |
| 58 |
https://github.com/unixpickle/anyrl-py |
Tensorflow |
| 59 |
https://github.com/Scitator/rl-course-experiments |
Tensorflow |
| 60 |
https://github.com/oxwhirl/pymarl |
PyTorch |
