dbobrenko/awesome-rl

Awesome RL: Papers, Books, Codes, Benchmarks

Awesome Reinforcement Learning

Click here to see icon descriptions.

• 🚀 - state-of-the-art agent/technique at the moment of paper publication.
• ⭐ - valuable paper.
• - Model-based RL.
• - Multi-Agent RL.
• - Self-Play.
• - Evolutionary & Genetic Algorithms.
• - Generalization on unseen environments.
• - Auto ML - Architecture search.
• - Manipulation tasks.
• - Locomotion: MuJoCo, Roboschool, etc.
• - Navigation tasks.
• - Strategy Planning Problems.
• - Transfer learning.
• - Inverse Reinforcement Learning.
• - Meta-Learning.
• - Curiosity Learning, Advanced Exploration.
• - Table games (Table).
• - Atari game (Atari).
• - Doom game (Doom).
• - Starcraft game (Starcraft).
• - Go game (Go).

Table of Contents

• Frameworks
• Benchmarks
• Policy-Based Generic Agents
• Value-Based Generic Agents
• Model-Based Generic Agents
• Evolutionary & Genetic Algorithms
• Exploration
• Self-Play
• Meta-Learning
• Multi-Agent RL
• Inverse RL
• Navigation
• Manipulation
• Locomotion
• Auto ML
• Other Domains
• Books
• Search for new Papers
• Misc

RL Frameworks & Implementations

[Stable Baselines3] PyTorch: MaskablePPO, PPO, A2C, DQN, etc

[Baselines @ OpenAI] TensorFlow: PPO, A2C, DQN, TRPO, ACKTR, DDPG, HER, GAIL, etc

[Baselines @ DLR-RM] Pytorch: Custom envs, custom policies

[RLlib @ Ray Pytorch / TensorFlow]

[Dopamine @ Google] TensorFlow: Rainbow, c51, IQN, DQN, etc

[TensorForce] TensorFlow: A3C, PPO, TRPO, DQN, etc

[pytorch-a2c-ppo-acktr] PyTorch: A2C, ACKTR, PPO, GAIL, etc

RL Benchmarks

[OpenAI Benchmarks for PPO, A2C, ACKTR, ACER]

[OpenAI Benchmarks for DQN, Double DQN, Dueling DQN, Prioritized DQN]

[Google Benchmarks for Rainbow, c51, IQN, DQN]

Policy-Based Generic Agents

🚀 [Soft Actor Critic] [blog] [code] 2018 @ Google Brain, UC Berkeley

🚀 [IMPALA] 2018 @ Uber AI Labs

🚀 [Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR, A2C)] 2018; Univ. of Toronto, New York Univ.

🚀 [Proximal Policy Optimization Algorithms (PPO)] [blog] 2017 @ OpenAI

🚀 📝 Notes [Asynchronous Methods for Deep Reinforcement Learning (A3C)] 2016 @ Google Deepmind

[High-dimensional continuous control using generalized advantage estimation (GAE)] 2015 @ Berkeley

⭐ [Trust Region Policy Optimization (TRPO)] 2015 @ UC Berkeley

⭐ [Actor-Critic Algorithms, pdf] Konda and Tsitsiklis, 2003

⭐ [Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning (REINFORCE), pdf] Ronald J. Williams, 1992 @ Northeastern Univ.

Value-Based Generic Agents

🚀 [Implicit Quantile Networks for Distributional Reinforcement Learning (IQN)] Dabney et al., 2018 @ Google Deepmind

🚀 [A Distributional Perspective on Reinforcement Learning (c51)] Bellemare et al., 2018 @ Google Deepmind

🚀 [Rainbow: Combining Improvements in Deep Reinforcement Learning] Hessel et al., 2017 @ Google Deepmind

🚀 [Dueling Network Architectures for Deep Reinforcement Learning (Dueling DQN)] Wang et al., 2015 @ Google Deepmind

🚀 📝 Notes [Prioritized Experience Replay] Schaul et al., 2015 @ Google Deepmind

🚀 [Deep Reinforcement Learning with Double Q-learning (Double DQN)] Hasselt et al., 2015 @ Google Deepmind

🚀 📝 Notes [Human-level control through deep reinforcement learning (DQN)] [pdf] Mnih et al., 2015 @ Google Deepmind

🚀 [Playing Atari with Deep Reinforcement Learning** (DQN)] Mnih et al., 2013 @ DeepMind Technologies

⭐ [Temporal Difference Learning and TD-Gammon, pdf] Gerald Tesauro, 1995

Model-Based Generic Agents

[Model-Based Reinforcement Learning for Atari] 2019 @ Google Brain, etc

⭐ [World Models] [blog] 2018 @ IDSIA, Google Brain, NNAISENSE

[Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning] [blog] [code] 2017 @ Berkeley

[Learning model-based planning from scratch], [blog] 2017 @ Google DeepMind

[The Predictron: End-To-End Learning and Planning] 2016 @ Google Deepmind

Evolutionary Algorithms

[Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari] 2018 @ Univ. of Freiburg

⭐ [Deep Neuroevolution] 2017 @ Uber AI Labs

⭐ [Evolution Strategies as a Scalable Alternative to Reinforcement Learning] 2017 @ OpenAI

[Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning, pdf] 2013 @ IDSIA, USI-SUPSI

Exploration

🚀 [Go-Explore] 2019 @ Uber AI Labs

[Exploration by Random Network Distillation (RND)] [blog] [code] 2018 @ OpenAI

[Large-Scale Study of Curiosity-Driven Learning] [blog] 2018 @ OpenAI, Berkeley, Univ. of Edinburgh

⭐ [RUDDER: Return Decomposition for Delayed Rewards] [code] 2018 @ Johannes Kepler Univ. Linz

[Deep Curiosity Search] 2018 @ Univ. of Wyoming

[Parameter Space Noise for Exploration] 2017 @ OpenAI, Karlsruhe Inst. of Tech.

⭐ [Imagination-Augmented Agents for Deep Reinforcement Learning (I2As)] [blog] 2017 @ DeepMind

Self-Play

⭐ [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm] Silver et al., 2017 @ Google Deepmind

⭐ [Mastering the Game of Go without Human Knowledge (AlphaGo Zero), pdf], [blog] Silver et al., 2017 @ Deepmind

[Mastering the game of Go with deep neural networks and tree search (AlphaGo Master)], [reddit] Silver et al., 2017 @ Deepmind, Google

Meta-Learning

[Meta Learning Shared Hierarchies] [blog] Frans et al., 2017 @ OpenAI, Berkeley.

[Hybrid Reward Architecture for Reinforcement Learning (HRA)] van Seijen et al., 2017 @ Microsoft Maluuba, McGill Univ.

Multi-Agent RL

[Learning with Opponent-Learning Awareness (LOLA)] [blog] Foerster et al., 2017 @ OpenAI, Oxford, Berkeley, CMU

Inverse RL

[SFV: Reinforcement Learning of Physical Skills from Videos] [blog] Peng et al., 2018; Berkeley

[One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning] Finn et al., 2018 @ UC Berkeley

[One-Shot Visual Imitation Learning via Meta-Learning] Finn et al., 2017 @ UC Berkeley, OpenAI

Navigation

[Learning to Navigate in Cities Without a Map] Mirowski et al, 2019 @ Deepmind

[Human-level performance in first-person multiplayer games with population-based deep reinforcement learning] [blog] Jaderberg et al, 2018 @ DeepMind

[Building Generalizable Agents with a Realistic and Rich 3D Environment] Wu et al, 2018 @ Berkeley, FAIR

🚀 [Learning to Navigate in Complex Environments] Mirowski et al., 2017 @ Deepmind

Distral: Robust Multitask Reinforcement Learning] Teh et al, 2017 @ Deepmind

[RL²: Fast Reinforcement Learning via Slow Reinforcement Learning] Duan et al., 2016 @ Berkeley, OpenAI

⭐ 📝 Notes [Reinforcement Learning with unsupervised auxiliary tasks (UNREAL)] Jaderberg et al., 2016 @ Google DeepMind

🚀 [Learning to act by predicting the future (VizDoom 2016 Full DM Winner)] Dosovitskiy, Koltun, 2016 @ Intel Labs

[Playing FPS Games with Deep Reinforcement Learning (VizDoom 2016 Limited DM 2nd place)] Lample, Chaplot, 2016 @ CMU

Manipulation

[Learning Dexterous In-Hand Manipulation] [blog] Andrychowicz et al., 2018 @ OpenAI

[Asymmetric Actor Critic for Image-Based Robot Learning] [blog] Pinto et al., 2017 @ OpenAI, CMU

[Sim-to-Real Transfer of Robotic Control with Dynamics Randomization], [blog] Peng et al., 2017 @ OpenAI, Berkeley

Locomotion

[Emergence of Locomotion Behaviours in Rich Environments] [blog] Heess et al., 2017 @ DeepMind

[Programmable Agents] Denil et al., 2017 @ Google Deepmind

Auto ML

[AutoAugment: Learning Augmentation Policies from Data] Cubuk et al., 2018 @ Google Brain

⭐ [Regularized Evolution for Image Classifier Architecture Search] Real et al., 2018 @ Google Brain

⭐ [Learning Transferable Architectures for Scalable Image Recognition] Zoph et al., 2017 @ Google Brain

[Neural Optimizer Search with Reinforcement Learning, pdf] Bello et al., 2017 @ Google Brain

[Neural Architecture Search with Reinforcement Learning] B. Zoph and Quoc V. Le, 2016 @ Google Brain

Other Domains

[A Deep Reinforcement Learning Chatbot] Serban et al., 2017 @ MILA

Books

⭐ [Reinforcement Learning: An Introduction, pdf] Richard S. Sutton and Andrew G. Barto, 2018

Search for new Papers

[A Brief Survey of Deep Reinforcement Learning] 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] S. Keshav, 2007 @ Univ. of Waterloo

[Transfromers: Attention is all you need] Vaswani et al. 2017 @ Google Brain/Research

awesome-rl's Issues

how to get the newest information of the RL paper

thank you! the recommended papers are all excellent. i want to know how i can get the the state of art paper in RL if you do not tell me by this github repository.how to find RL paper by myself?