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- 🚀 - 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).
- 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
[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
[OpenAI Benchmarks for PPO, A2C, ACKTR, ACER]
[OpenAI Benchmarks for DQN, Double DQN, Dueling DQN, Prioritized DQN]
[Google Benchmarks for Rainbow, c51, IQN, DQN]
🚀 [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.
🚀 [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 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
[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
🚀 [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
⭐ 
[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 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.
[Learning with Opponent-Learning Awareness (LOLA)] [blog] Foerster et al., 2017 @ OpenAI, Oxford, Berkeley, CMU
[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
[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
[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
[Emergence of Locomotion Behaviours in Rich Environments] [blog] Heess et al., 2017 @ DeepMind
[Programmable Agents] Denil et al., 2017 @ Google Deepmind
[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
[A Deep Reinforcement Learning Chatbot] Serban et al., 2017 @ MILA
⭐ [Reinforcement Learning: An Introduction, pdf] Richard S. Sutton and Andrew G. Barto, 2018
[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/
[How to Read a Paper] S. Keshav, 2007 @ Univ. of Waterloo
[Transfromers: Attention is all you need] Vaswani et al. 2017 @ Google Brain/Research