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Reinforcement Learning Git Repositories#
| Sno. | URL | Description |
|---|---|---|
| 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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