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Awesome RL: Papers, Books, Codes, Benchmarks
https://github.com/dbobrenko/awesome-rl

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Awesome RL: Papers, Books, Codes, Benchmarks

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README 

        
# Awesome Reinforcement Learning



  Click here to see icon descriptions.

  

- :rocket: - state-of-the-art agent/technique at the moment of paper publication.

- :star: - valuable paper.

- ![model-based] - Model-based RL.

- ![multi-agent-rl] - Multi-Agent RL.

- ![self-play] - Self-Play.

- ![evolution] - Evolutionary & Genetic Algorithms.

- ![generalization] - Generalization on unseen environments.

- ![auto-ml] - Auto ML - Architecture search.

- ![manipulation] - Manipulation tasks.

- ![locomotion] - Locomotion: MuJoCo, Roboschool, etc.

- ![navigation] - Navigation tasks.

- ![plan] - Strategy Planning Problems.

- ![transfer] - Transfer learning.

- ![inverse-rl] - Inverse Reinforcement Learning.

- ![meta-learning] - Meta-Learning.

- ![exploration] - Curiosity Learning, Advanced Exploration.

- ![table] - Table games (Table).

- ![atari] - Atari game (Atari).

- ![doom] - Doom game (Doom).

- ![sc] - Starcraft game (Starcraft).

- ![go] - Go game (Go).



## Table of Contents

  - [Frameworks](#frameworks)

  - [Benchmarks](#benchmarks)

  - [Policy-Based Generic Agents](#policy-agents)

  - [Value-Based Generic Agents](#value-agents)

  - [![model-based] Model-Based Generic Agents](#model-based)

  - [![evolution] Evolutionary & Genetic Algorithms](#evolution)

  - [![exploration] Exploration](#exploration)

  - [![self-play] Self-Play](#self-play)

  - [![meta-learning] Meta-Learning](#meta-learning)

  - [![multi-agent-rl] Multi-Agent RL](#multi-agent-rl)

  - [![inverse-rl] Inverse RL](#inverse-rl)

  - [![navigation] Navigation](#navigation)

  - [![manipulation] Manipulation](#manipulation)

  - [![locomotion] Locomotion](#locomotion)

  - [![auto-ml] Auto ML](#auto-ml)

  - [Other Domains](#other-domains)

  - [Books](#books)

  - [Search for new Papers](#search-for-new-papers)

  - [Misc](#misc)



## RL Frameworks & Implementations 

[[Stable Baselines3](https://stable-baselines3.readthedocs.io/en/master/)] PyTorch: MaskablePPO, PPO, A2C, DQN, etc



[[Baselines @ OpenAI](https://github.com/openai/baselines)] TensorFlow: PPO, A2C, DQN, TRPO, ACKTR, DDPG, HER, GAIL, etc



[[Baselines @ DLR-RM](https://github.com/DLR-RM/stable-baselines3)] Pytorch: Custom envs, custom policies

  

[[RLlib @ Ray](https://github.com/ray-project/ray/tree/master/rllib) Pytorch / TensorFlow]

  

[[Dopamine @ Google](https://github.com/google/dopamine)] TensorFlow: Rainbow, c51, IQN, DQN, etc

 

[[TensorForce](https://github.com/reinforceio/tensorforce)] TensorFlow: A3C, PPO, TRPO, DQN, etc



[[pytorch-a2c-ppo-acktr](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail)] PyTorch: A2C, ACKTR, PPO, GAIL, etc



## RL Benchmarks 

[[OpenAI Benchmarks for PPO, A2C, ACKTR, ACER](https://github.com/openai/baselines-results/blob/master/acktr_ppo_acer_a2c_atari.ipynb)]

 

[[OpenAI Benchmarks for DQN, Double DQN, Dueling DQN, Prioritized DQN](https://github.com/openai/baselines-results/blob/master/dqn_results.ipynb)]



[[Google Benchmarks for Rainbow, c51, IQN, DQN](https://google.github.io/dopamine/baselines/plots.html)]



## Policy-Based Generic Agents 

:rocket: [[Soft Actor Critic](https://arxiv.org/abs/1812.05905)] [[blog](https://ai.googleblog.com/2019/01/soft-actor-critic-deep-reinforcement.html)] [[code](https://github.com/rail-berkeley/softlearning/)] 2018 @ Google Brain, UC Berkeley



:rocket: [[IMPALA](https://arxiv.org/abs/1802.01561v2)] 2018 @ Uber AI Labs

  

:rocket: [[Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR, A2C)](https://arxiv.org/abs/1708.05144)] 2018; Univ. of Toronto, New York Univ.



:rocket: [[Proximal Policy Optimization Algorithms (PPO)](https://arxiv.org/abs/1707.06347)] [[blog](https://blog.openai.com/openai-baselines-ppo/)] 2017 @ OpenAI



:rocket: [:pencil: Notes](./notes/a3c-agent.md) [[Asynchronous Methods for Deep Reinforcement Learning (A3C)](https://arxiv.org/abs/1602.01783v2)] 2016 @ Google Deepmind 



[[High-dimensional continuous control using generalized advantage estimation (GAE)](https://arxiv.org/abs/1506.02438)] 2015 @ Berkeley



:star: [[Trust Region Policy Optimization (TRPO)](https://arxiv.org/abs/1502.05477)] 2015 @ UC Berkeley



:star: [[Actor-Critic Algorithms, pdf](https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf)] Konda and Tsitsiklis, 2003



:star: [[Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning (REINFORCE), pdf](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)] Ronald J. Williams, 1992 @ Northeastern Univ.



## Value-Based Generic Agents 

:rocket: [[Implicit Quantile Networks for Distributional Reinforcement Learning (IQN)](https://arxiv.org/abs/1806.06923)] Dabney et al., 2018 @ Google Deepmind



:rocket: [[A Distributional Perspective on Reinforcement Learning (c51)](https://arxiv.org/abs/1707.06887)] Bellemare et al., 2018 @ Google Deepmind



:rocket: [[Rainbow: Combining Improvements in Deep Reinforcement Learning](https://arxiv.org/abs/1710.02298)] Hessel et al., 2017 @ Google Deepmind



:rocket: [[Dueling Network Architectures for Deep Reinforcement Learning (Dueling DQN)](https://arxiv.org/abs/1511.06581)] Wang et al., 2015 @ Google Deepmind



:rocket: [:pencil: Notes](./notes/prioritized-exp-replay.md) [[Prioritized Experience Replay](https://arxiv.org/abs/1511.05952v4)] Schaul et al., 2015 @ Google Deepmind



:rocket: [[Deep Reinforcement Learning with Double Q-learning (Double DQN)](https://arxiv.org/abs/1509.06461)] Hasselt et al., 2015 @ Google Deepmind



:rocket: [:pencil: Notes](./notes/dqn-agent.md) [[Human-level control through deep reinforcement learning (DQN)](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)]

[[pdf](hhttp://www.readcube.com/articles/10.1038/nature14236?shared_access_token=Lo_2hFdW4MuqEcF3CVBZm9RgN0jAjWel9jnR3ZoTv0P5kedCCNjz3FJ2FhQCgXkApOr3ZSsJAldp-tw3IWgTseRnLpAc9xQq-vTA2Z5Ji9lg16_WvCy4SaOgpK5XXA6ecqo8d8J7l4EJsdjwai53GqKt-7JuioG0r3iV67MQIro74l6IxvmcVNKBgOwiMGi8U0izJStLpmQp6Vmi_8Lw_A%3D%3D)]

Mnih et al., 2015 @ Google Deepmind



:rocket: [[Playing Atari with Deep Reinforcement Learning** (DQN)](https://arxiv.org/abs/1312.5602)] Mnih et al., 2013 @ DeepMind Technologies



:star: [[Temporal Difference Learning and TD-Gammon, pdf](http://cling.csd.uwo.ca/cs346a/extra/tdgammon.pdf)] Gerald Tesauro, 1995



## ![model-based] Model-Based Generic Agents  

[[Model-Based Reinforcement Learning for Atari](https://arxiv.org/abs/1903.00374)] 2019 @ Google Brain, etc



:star: ![navigation] [[World Models](https://arxiv.org/abs/1803.10122)] [[blog](https://worldmodels.github.io/)] 2018 @ IDSIA, Google Brain, NNAISENSE



![locomotion] [[Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning](https://arxiv.org/abs/1708.02596)] [[blog](http://bair.berkeley.edu/blog/2017/11/30/model-based-rl/)] [[code](https://github.com/nagaban2/nn_dynamics)] 2017 @ Berkeley



![locomotion] [[Learning model-based planning from scratch](https://arxiv.org/abs/1707.06170)], [[blog](https://deepmind.com/blog/agents-imagine-and-plan/)] 2017 @ Google DeepMind



![navigation] [[The Predictron: End-To-End Learning and Planning](https://arxiv.org/abs/1612.08810v2)] 2016 @ Google Deepmind



## ![evolution] Evolutionary Algorithms 

[[Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari](https://arxiv.org/abs/1802.08842)] 2018 @ Univ. of Freiburg



:star: ![locomotion] [[Deep Neuroevolution](https://arxiv.org/abs/1712.06567)] 2017 @ Uber AI Labs



:star: [[Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://arxiv.org/abs/1703.03864v1)] 2017 @ OpenAI



[[Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning, pdf](http://people.idsia.ch/~juergen/gecco2013torcs.pdf)] 2013 @ IDSIA, USI-SUPSI



## ![exploration] Exploration 

:rocket: [[Go-Explore](https://arxiv.org/abs/1901.10995)] 2019 @ Uber AI Labs



[[Exploration by Random Network Distillation (RND)](https://arxiv.org/abs/1810.12894)] [[blog](https://blog.openai.com/reinforcement-learning-with-prediction-based-rewards/)] [[code](https://github.com/openai/random-network-distillation)] 2018 @ OpenAI



![navigation] [[Large-Scale Study of Curiosity-Driven Learning](https://arxiv.org/abs/1808.04355)] [[blog](https://pathak22.github.io/large-scale-curiosity/)] 2018 @ OpenAI, Berkeley, Univ. of Edinburgh



:star: [[RUDDER: Return Decomposition for Delayed Rewards](https://arxiv.org/abs/1806.07857)] [[code](https://github.com/ml-jku/baselines-rudder)] 2018 @ Johannes Kepler Univ. Linz



[[Deep Curiosity Search](https://arxiv.org/abs/1806.00553)] 2018 @ Univ. of Wyoming



![locomotion] [[Parameter Space Noise for Exploration](https://arxiv.org/abs/1706.01905)] 2017 @ OpenAI, Karlsruhe Inst. of Tech.



:star: ![transfer] [[Imagination-Augmented Agents for Deep Reinforcement Learning (I2As)](https://arxiv.org/abs/1707.06203)] [[blog](https://deepmind.com/blog/agents-imagine-and-plan/)] 2017 @ DeepMind



## ![self-play] Self-Play 

:star: ![table] [[Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://arxiv.org/abs/1712.01815)] Silver et al., 2017 @ Google Deepmind



:star: ![table] [[Mastering the Game of Go without Human Knowledge (AlphaGo Zero), pdf](https://deepmind.com/documents/119/agz_unformatted_nature.pdf)], [[blog](https://deepmind.com/blog/alphago-zero-learning-scratch/)] Silver et al., 2017 @ Deepmind



![table] [[Mastering the game of Go with deep neural networks and tree search (AlphaGo Master)](https://www.nature.com/nature/journal/v529/n7587/full/nature16961.html)], [[reddit](https://www.reddit.com/r/MachineLearning/comments/42ytdx/pdf_mastering_the_game_of_go_with_deep_neural/)] Silver et al., 2017 @ Deepmind, Google



## ![meta-learning] Meta-Learning

![locomotion] [[Meta Learning Shared Hierarchies](https://arxiv.org/abs/1710.09767)] [[blog](https://blog.openai.com/learning-a-hierarchy/)] Frans et al., 2017 @ OpenAI, Berkeley.



[[Hybrid Reward Architecture for Reinforcement Learning (HRA)](https://arxiv.org/abs/1706.04208v1)] van Seijen et al., 2017 @ Microsoft Maluuba, McGill Univ.



## ![multi-agent-rl] Multi-Agent RL

[[Learning with Opponent-Learning Awareness (LOLA)](https://arxiv.org/abs/1709.04326)] [[blog](https://blog.openai.com/learning-to-model-other-minds/)] Foerster et al., 2017 @ OpenAI, Oxford, Berkeley, CMU



## ![inverse-rl] Inverse RL 

![manipulation] [[SFV: Reinforcement Learning of Physical Skills from Videos](https://arxiv.org/abs/1810.03599)] [[blog](https://bair.berkeley.edu/blog/2018/10/09/sfv/)] Peng et al., 2018; Berkeley



![manipulation] [[One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://arxiv.org/abs/1802.01557v1)] Finn et al., 2018 @ UC Berkeley

  

![manipulation] [[One-Shot Visual Imitation Learning via Meta-Learning](https://arxiv.org/abs/1709.04905)] Finn et al., 2017 @ UC Berkeley, OpenAI



## ![navigation] Navigation 

[[Learning to Navigate in Cities Without a Map](https://arxiv.org/abs/1804.00168)] Mirowski et al, 2019 @ Deepmind



[[Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://arxiv.org/abs/1807.01281)] [[blog](https://deepmind.com/blog/capture-the-flag/)] Jaderberg et al, 2018 @ DeepMind



![generalization] [[Building Generalizable Agents with a Realistic and Rich 3D Environment](https://arxiv.org/abs/1801.02209)] Wu et al, 2018 @ Berkeley, FAIR



:rocket: [[Learning to Navigate in Complex Environments](https://arxiv.org/abs/1611.03673)] Mirowski et al., 2017 @ Deepmind



![transfer] [Distral: Robust Multitask Reinforcement Learning](https://arxiv.org/abs/1707.04175)] Teh et al, 2017 @ Deepmind



![meta-learning] [[RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://arxiv.org/abs/1611.02779)] Duan et al., 2016 @ Berkeley, OpenAI



:star: [:pencil: Notes](./notes/unreal-agent.md) ![locomotion] [[Reinforcement Learning with unsupervised auxiliary tasks (UNREAL)](https://arxiv.org/abs/1611.05397)] Jaderberg et al., 2016 @ Google DeepMind



:rocket: [[Learning to act by predicting the future (VizDoom 2016 Full DM Winner)](https://arxiv.org/abs/1611.01779)] Dosovitskiy, Koltun, 2016 @ Intel Labs



[[Playing FPS Games with Deep Reinforcement Learning (VizDoom 2016 Limited DM 2nd place)](https://arxiv.org/abs/1609.05521)] Lample, Chaplot, 2016 @ CMU



## ![manipulation] Manipulation 

![generalization] [[Learning Dexterous In-Hand Manipulation](https://arxiv.org/abs/1808.00177)] [[blog](https://blog.openai.com/learning-dexterity/)] Andrychowicz et al., 2018 @ OpenAI



![generalization] [[Asymmetric Actor Critic for Image-Based Robot Learning](https://arxiv.org/abs/1710.06542)] [[blog](https://blog.openai.com/generalizing-from-simulation/)] Pinto et al., 2017 @ OpenAI, CMU



![generalization] [[Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://arxiv.org/abs/1710.06537)], [[blog](https://blog.openai.com/generalizing-from-simulation/)] Peng et al., 2017 @ OpenAI, Berkeley



## ![locomotion] Locomotion 

[[Emergence of Locomotion Behaviours in Rich Environments](https://arxiv.org/abs/1707.02286)] [[blog](https://deepmind.com/blog/producing-flexible-behaviours-simulated-environments/)] Heess et al., 2017 @ DeepMind



[[Programmable Agents](https://arxiv.org/abs/1706.06383v1)] Denil et al., 2017 @ Google Deepmind



## ![auto-ml] Auto ML 

[[AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501)] Cubuk et al., 2018 @ Google Brain



:star: ![evolution] [[Regularized Evolution for Image Classifier Architecture Search](https://arxiv.org/abs/1802.01548v2)] Real et al., 2018 @ Google Brain



:star: [[Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012)] Zoph et al., 2017 @ Google Brain



[[Neural Optimizer Search with Reinforcement Learning, pdf](http://proceedings.mlr.press/v70/bello17a/bello17a.pdf)] Bello et al., 2017 @ Google Brain



[[Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578)] B. Zoph and Quoc V. Le, 2016 @ Google Brain



## Other Domains  

[[A Deep Reinforcement Learning Chatbot](https://arxiv.org/abs/1709.02349)] Serban et al., 2017 @ MILA



## Books

:star: [[Reinforcement Learning: An Introduction, pdf](http://incompleteideas.net/book/bookdraft2018jan1.pdf)] Richard S. Sutton and Andrew G. Barto, 2018



## Search for new Papers

[[A Brief Survey of Deep Reinforcement Learning](https://arxiv.org/abs/1708.05866)] Arulkumaran et al., 2017



Another Awesome Deep RL list: https://github.com/tigerneil/awesome-deep-rl



Awesome Offline RL: https://github.com/hanjuku-kaso/awesome-offline-rl



ArXiv Sanity Preserver: http://www.arxiv-sanity.com/

  

GitXiv: http://www.gitxiv.com/



## Misc

[[How to Read a Paper](http://ccr.sigcomm.org/online/files/p83-keshavA.pdf)] S. Keshav, 2007 @ Univ. of Waterloo



[Transfromers: [Attention is all you need](https://arxiv.org/abs/1706.03762)] Vaswani et al. 2017 @ Google Brain/Research



[atari]: ./icons/atari.png	"Atari 2600 games"

[doom]: ./icons/doom.png	"Doom game"

[sc]: ./icons/sc.png	"Starcraft game"

[go]: ./icons/go.png	"Go game"

[table]: ./icons/table.png	"Table games"

[auto-ml]: ./icons/nn.png	"Neural Networks & Optimizers"

[manipulation]: ./icons/robot.png	"Robotic hand manipuation"

[locomotion]: ./icons/loco.png	"Locomotion tasks"

[navigation]: ./icons/maze.png	"Navigation tasks"

[model-based]: ./icons/model.png	"Model-based RL"

[multi-agent-rl]: ./icons/marl.png	"Multi-Agent RL"

[self-play]: ./icons/sp.png	"Self-Play RL"

[evolution]: ./icons/evo.png	"Evolutionary & Genetic Algorithms"

[generalization]: ./icons/gener.png	"Generalization across environments"

[plan]: ./icons/plan.png	"Complex Planning Problems"

[transfer]: ./icons/transfer.png	"Transfer Learning"

[inverse-rl]: ./icons/irl.png	"Inverse Reinforcement Learning"

[meta-learning]: ./icons/meta.png	"Meta-Learning"

[exploration]: ./icons/sparse.png	"Curiosity learning, Exploration"