RL-Gallery

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A gallery for reinforcement learning, including frameworks, tutorials, papers, implementations, applications, etc.

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# RL-Gallery
* [Framework](#rl-framework)
* [Tutorials](#rl-tutorials)
* [Platform](#rl-platform)
* [Implementation](#rl-implementation)
* [Papers](#rl-papers)
* [Applications](#rl-applications)
* [Technique](#rl-technique)
------

# RL Framework
* [Spinning Up, OpenAI](https://spinningup.openai.com/)
* [Rllab, Berkeley RL Lab](https://github.com/rll/rllab)
* [Rllib, UC Berkeley’s RISE Lab, Ray Project](https://github.com/ray-project/ray/tree/master/python/ray/rllib/)
* [Rlkit, Vitchyr Pong's Implementations](https://github.com/vitchyr/rlkit)
* [Stable-baselines](https://github.com/hill-a/stable-baselines)
* [Baselines, OpenAI](https://github.com/openai/baselines)
* [TensorForce](https://github.com/reinforceio/tensorforce)
* [Keras RL](https://github.com/keras-rl/keras-rl)
* [Dopamine, Google](https://github.com/google/dopamine)
* [Coach, Intel](https://github.com/NervanaSystems/coach)
* [PARL, Baidu](https://github.com/PaddlePaddle/PARL)
* [torchrl, salmanazarr's implementations](https://github.com/salmanazarr/torchrl)
* [rlpack, liber145's implementations](https://github.com/liber145/rlpack)
* [ChainerRL, based on Chainer](https://github.com/chainer/chainerrl)
* [SLM-Lab, Wah Loon Keng's implementations](https://github.com/kengz/SLM-Lab)
* [adeptRL, heronsystems](https://github.com/heronsystems/adeptRL)
* [autonomous-learning-library, Chris Nota's implementations](https://github.com/cpnota/autonomous-learning-library)
* [rlgraph, RLgraph](https://github.com/rlgraph/rlgraph)
* [rllite, lite repository for RL](https://github.com/ZJU-RL/rllite)

# RL Tutorials

- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/bookdraft2017nov5.pdf)
- [CS 294-112, UC Berkeley](http://rail.eecs.berkeley.edu/deeprlcourse/)
- [David Silver's courses](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
- [Deep RL Bootcamp, UC Berkeley](https://sites.google.com/view/deep-rl-bootcamp/lectures)
- [MIT 6.S094: Deep Learning for Self-Driving Cars 2018 Lecture 3 Notes: Deep Reinforcement Learning](https://hackernoon.com/mit-6-s094-deep-learning-for-self-driving-cars-2018-lecture-3-notes-deep-reinforcement-learning-fe9a8592e14a)
- [UCL Advanced Deep Learning & Reinforcement Learning](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)
- [CS234: Reinforcement Learning Winter 2019, Stanford](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)
- [awesome-deep-rl](https://github.com/tigerneil/awesome-deep-rl)
- [An Introduction to Deep Reinforcement Learning](https://arxiv.org/pdf/1811.12560v2.pdf)
- [Morvan Reinforcement Learning](https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/)
- [PyTorch tutorials of RL](https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html)
- [Arthur Juliani's blog](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0)
- [Thomas Simonini's DRL course](https://www.simoninithomas.com/)
- [Lilian Weng's blog](https://lilianweng.github.io/lil-log/2018/02/19/a-long-peek-into-reinforcement-learning.html)
- [Andrej Karpathy's blog](http://karpathy.github.io/2016/05/31/rl/)
- [Hung-yi Lee's DRL lectures (Chinese version)](http://t.cn/RBtg1O2)
- [Spinning Up in Deep RL Workshop, OpenAI](https://www.youtube.com/watch?v=fdY7dt3ijgY)
- [Reproducible, Reusable, and Robust RL, Joelle Pineau](https://www.youtube.com/watch?v=Kee4ch3miVA)
- [Udacity, RL by Georgia Tech](https://www.udacity.com/course/reinforcement-learning--ud600)
- [Deep Learning Summer School, Montreal 2016](http://videolectures.net/deeplearning2016_montreal/)
- [CS 287: Advanced Robotics, Fall 2015](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa15/)
- [RLKorea (Korean version)](https://reinforcement-learning-kr.github.io/)
- [phd-bibliography](https://github.com/eleurent/phd-bibliography)

# RL Platform
* [Gym, OpenAI](https://gym.openai.com/docs/)
* [Universe, OpenAI](https://blog.openai.com/universe/)
* [Roboschool, OpenAI](https://github.com/openai/roboschool)
* [PlayGround: AI Research into Multi-Agent Learning](https://github.com/MultiAgentLearning/playground)
* [Neural MMO: A Massively Multiagent Game Environment](https://github.com/openai/neural-mmo)
* [ELF, a platform for game research, Facebook](https://github.com/pytorch/ELF)
* [Reaver, StarCraft II](https://github.com/inoryy/reaver-pysc2)
* [Pybullet: Real-Time Physics Simulation](https://pybullet.org)
* [highway driving environments](https://github.com/eleurent/highway-env)
* [ma-gym](https://github.com/koulanurag/ma-gym)
* [multiworld, Vitchyr Pong](https://github.com/vitchyr/multiworld)

# RL Implementation
* [RL-Adventure, Dulat Yerzat's implementation](https://github.com/higgsfield/RL-Adventure)
* [RL-Adventure2, Dulat Yerzat's implementation](https://github.com/higgsfield/RL-Adventure-2)
* [John Schulman's RL repository](https://github.com/joschu/modular_rl)
* [TianhongDai's implementations](https://github.com/TianhongDai/reinforcement-learning-algorithms)
* [Denny Britz's implementations](https://github.com/dennybritz/reinforcement-learning)
* [steveKapturowski's implementations](https://github.com/steveKapturowski/tensorflow-rl)
* [DeepRL, ShangtongZhang's implementations](https://github.com/ShangtongZhang/DeepRL)
* [reinforcement-learning-an-introduction, ShangtongZhang's implementations](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
* [Reinforcement-learning-with-tensorflow, Morvan Zhou's implementations](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow)
* [mario_rl, Chanwoong joo's implementations](https://github.com/jcwleo/mario_rl)
* [PyTorch-RL, Ye Yuan's implementations](https://github.com/Khrylx/PyTorch-RL)
* [pytorchrl, Ermo Wei's implementations](https://github.com/nosyndicate/pytorchrl)
* [Entropy-Regularized-RL, lihaoruo's implementations](https://github.com/LihaoR/Entropy-Regularized-RL)
* [Tidy-Reinforcement-learning, sarcturus00's implementations](https://github.com/sarcturus00/Tidy-Reinforcement-learning)
* [Random Network Distillation, jcwleo's implementation](https://github.com/jcwleo/random-network-distillation-pytorch)
* [rl-baselines-zoo, pre-trained RL agents using stable-baselines](https://github.com/araffin/rl-baselines-zoo)
* [rl-agents, Edouard Leurent's implementations](https://github.com/eleurent/rl-agents)
* [pytorch-rl, Navneet M Kumar's implementations](https://github.com/navneet-nmk/pytorch-rl)
* [lets-do-irl, Reinforcement Learning KR's implementations](https://github.com/reinforcement-learning-kr/lets-do-irl)
* [tensorflow_practice/rl, princewen's implementations](https://github.com/princewen/tensorflow_practice/tree/master/RL)
* [self-imitation-learning, Junhyuk Oh's implementations](https://github.com/junhyukoh/self-imitation-learning)
* [pytorch-a2c-ppo-acktr-gail, Ilya Kostrikov's implementations](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail)
* [Deep_RL_with_pytorch, sungyubkim's implementations](https://github.com/sungyubkim/Deep_RL_with_pytorch)
* [pytorch-madrl, Chenglong Chen's implementations](https://github.com/ChenglongChen/pytorch-madrl)
* [Deep-reinforcement-learning-with-pytorch, Johnny He's implementations](https://github.com/sweetice/Deep-reinforcement-learning-with-pytorch)
* [Sam Greydanus's A3C implementation](https://github.com/greydanus/baby-a3c)
* [TD3 implementation](https://github.com/sfujim/TD3)
* [SAC implementation](https://github.com/haarnoja/sac)
* [simple-A2C, Rudy Gilman's implementations](https://github.com/rgilman33/simple-A2C)
* [DeepPILCO, zuoxingdong's implementation](https://github.com/zuoxingdong/DeepPILCO)
* [FeUdal-montezuma, Woongwon Lee's implementations](https://github.com/dnddnjs/feudal-montezuma)
* [Tidy-Reinforcement-learning, sarcturus00's implementations](https://github.com/sarcturus00/Tidy-Reinforcement-learning)
* [learning-to-communicate-pytorch, Minqi's implementations](https://github.com/minqi/learning-to-communicate-pytorch)
* [pytorch-cpp-rl, Isaac Poulton's implementations](https://github.com/Omegastick/pytorch-cpp-rl)
* [deep-rl, Pedro Morais's implementations](https://github.com/p-morais/deep-rl)
* [Inverse-Reinforcement-Learning, Matthew Alger's implementations](https://github.com/MatthewJA/Inverse-Reinforcement-Learning)
* [ReinforcementLearning-AtariGame, Nasrudin Bin Salim's implementations](https://github.com/Nasdin/ReinforcementLearning-AtariGame)
* [RND-Pytorch, WizDom13's implementations](https://github.com/wizdom13/RND-Pytorch)
* [Deep-Reinforcement-Learning-Algorithms-with-PyTorch, Petros Christodoulou's implementations](https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch)
* [reinforcement_learning, Yiren Lu's implementations](https://github.com/yrlu/reinforcement_learning)
* [rl_a3c_pytorch, David Griffis's implementations](https://github.com/dgriff777/rl_a3c_pytorch)
* [RL-Experiments, Yanhua Huang's implementations](https://github.com/Officium/RL-Experiments)
* [Hierarchical-Actor-Critc-HAC-, Andrew Levy's implementation](https://github.com/andrew-j-levy/Hierarchical-Actor-Critc-HAC-)
* [tensorflow_RL, RLOpensource](https://github.com/RLOpensource/tensorflow_RL)
* [noreward-rl, Deepak Pathak's implementations](https://github.com/pathak22/noreward-rl)
* [robotics-rl-srl, Antonin RAFFIN's implementations](https://github.com/araffin/robotics-rl-srl)
* [Super-Mario-Bros-RL, Amine SADEQ's implementations](https://github.com/sadeqa/Super-Mario-Bros-RL)
* [rltf, Nikolay Nikolov's implementations](https://github.com/nikonikolov/rltf.git)
* [reward-learning-rl, Avi Singh's implementation](https://github.com/avisingh599/reward-learning-rl)
* [Deep-Reinforcement-Learning-Algorithms-with-PyTorch, Petros Christodoulou's implementations](https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch)
* [rl-starter-files, Lucas Willems's implementation](https://github.com/lcswillems/rl-starter-files)
* [rl_a3c_pytorch, David Griffis's implementation](https://github.com/dgriff777/rl_a3c_pytorch)
* [DRL, createamind's implementations](https://github.com/createamind/DRL)
* [lagom, Xingdong Zuo's implementations](https://github.com/zuoxingdong/lagom)
* [rltime, Opher Lieber's implementations](https://github.com/opherlieber/rltime)
* [rl_algorithms, Medipixel](https://github.com/medipixel/rl_algorithms)
* [rlpyt, astooke's implementations](https://github.com/astooke/rlpyt)

# RL Papers
* [Model-based-papers](https://github.com/danfeiX/model-based-papers)
* [DRL papers 2015-2016, Junhyuk Oh](https://github.com/junhyukoh/deep-reinforcement-learning-papers)
* [DRL papers 2015-2016, Yasuhiro Fujita](https://github.com/muupan/deep-reinforcement-learning-papers)
* [MARL-Papers](https://github.com/LantaoYu/MARL-Papers)
* [Spinning up, OpenAI](https://spinningup.openai.com/en/latest/spinningup/keypapers.html)

# RL Applications
* [Play Atari games, DeepMind](https://arxiv.org/pdf/1312.5602v1.pdf)
* [AlphaGo & AlphaGoZero, playing Go, DeepMind](https://deepmind.com/research/alphago/)
* [AlphaStar, playing StarCraft2, DeepMind](https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/)
* [Autonomous helicopter flight, Stanford University](http://heli.stanford.edu/)
* [Skill learning in 3D simulator, Xue Bin Peng](https://arxiv.org/pdf/1804.02717.pdf)
* [Agile locomotion for quadruped robots, Jie Tan](https://arxiv.org/pdf/1804.10332.pdf)
* [ANYmal robot skill learning, Synced](https://syncedreview.com/2019/01/23/you-cant-keep-an-rl-powered-anymal-down/)
* [Modular legged robot skill learning, Disney Research](https://la.disneyresearch.com/publication/automated-deep-reinforcement-learning-environment-for-hardware-of-a-modular-legged-robot/)
* [RL in business (Chinese version), Alibaba](http://techforum-img.cn-hangzhou.oss-pub.aliyun-inc.com/1517812754285/reinforcement_learning.pdf)
* [LOXM, executing trades, J.P.Morgan](https://www.jpmorgan.com/global/LOXM)

# RL Technique

| Technique                 | Benefit                                   | Mentioned Key Algorithm                                      |
| ------------------------- | ----------------------------------------- | ------------------------------------------------------------ |
| Target network            | Stabilize the training process            | [DQN, 2015](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)  |
| Memory buffer             | Breaking data relevance                   | [DQN, 2015](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)  |
| KL-constrained update     | Optimize update step size                 | [TRPO, 2015](https://arxiv.org/pdf/1502.05477.pdf)           |
| Advantage function        | Stabilize learning                        | [A3C, 2015](https://arxiv.org/pdf/1602.01783.pdf)            |
| Importance sampling       | Data efficient                            | [PER,2016](https://arxiv.org/pdf/1511.05952.pdf)             |
| Entropy-regularized       | Better exploration                        | [Soft Q-Learning, 2018](https://arxiv.org/pdf/1704.06440.pdf) |
| Boltzmann policy          | Richer mathematical meaning               | [Soft Q-Learning, 2018](https://arxiv.org/pdf/1704.06440.pdf) |
| Target policy smoothing   | Avert Q-function incorrect sharp peak     | [TD3, 2018](https://arxiv.org/pdf/1802.09477.pdf)            |
| Clipped double-Q learning | Fend off overestimation in the Q-function | [TD3, 2018](https://arxiv.org/pdf/1802.09477.pdf)            |
| Reparameterize the policy | Lower variance estimate                   | [SAC, 2018](https://arxiv.org/pdf/1801.01290.pdf)            |

**PS: "Mentioned Key Algorithm" may not be the first algorithm that uses this technique, but makes a detailed explanation**

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