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 |
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