Awesome Reinforcement Learning

A curated list of resources dedicated to reinforcement learning.

We have pages for other topics: awesome-rnn, awesome-deep-vision, awesome-random-forest

Maintainers: Hyunsoo Kim, Jiwon Kim

We are looking for more contributors and maintainers!

Contributing

Please feel free to pull requests

Table of Contents

Codes

Theory

Lectures

Books

Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction [Book] [Code]

Csaba Szepesvari, Algorithms for Reinforcement Learning [Book]

David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents [Book Chapter]

Dimitri P. Bertsekas and John N. Tsitsiklis, Neuro-Dynamic Programming [Book (Amazon)] [Summary]

Mykel J. Kochenderfer, Decision Making Under Uncertainty: Theory and Application [Book (Amazon)]

Surveys

Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore, Reinforcement Learning: A Survey, JAIR, 1996. [Paper]

S. S. Keerthi and B. Ravindran, A Tutorial Survey of Reinforcement Learning, Sadhana, 1994. [Paper]

Jens Kober, J. Andrew Bagnell, Jan Peters, Reinforcement Learning in Robotics, A Survey, IJRR, 2013. [Paper]

Littman, Michael L. "Reinforcement learning improves behaviour from evaluative feedback." Nature 521.7553 (2015): 445-451. [Paper]

Marc P. Deisenroth, Gerhard Neumann, Jan Peter, A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, 2014. [Book]

Papers / Thesis

Foundational Papers Marvin Minsky, Steps toward Artificial Intelligence, Proceedings of the IRE, 1961. [Paper] discusses issues in RL such as the "credit assignment problem" Ian H. Witten, An Adaptive Optimal Controller for Discrete-Time Markov Environments, Information and Control, 1977. [Paper] earliest publication on temporal-difference (TD) learning rule.

Methods Dynamic Programming (DP): Christopher J. C. H. Watkins, Learning from Delayed Rewards, Ph.D. Thesis, Cambridge University, 1989. [Thesis] Monte Carlo: Andrew Barto, Michael Duff, Monte Carlo Inversion and Reinforcement Learning, NIPS, 1994. [Paper] Satinder P. Singh, Richard S. Sutton, Reinforcement Learning with Replacing Eligibility Traces, Machine Learning, 1996. [Paper] Temporal-Difference: Richard S. Sutton, Learning to predict by the methods of temporal differences. Machine Learning 3: 9-44, 1988. [Paper] Q-Learning (Off-policy TD algorithm): Chris Watkins, Learning from Delayed Rewards, Cambridge, 1989. [Thesis] Sarsa (On-policy TD algorithm): G.A. Rummery, M. Niranjan, On-line Q-learning using connectionist systems, Technical Report, Cambridge Univ., 1994. [Report] Richard S. Sutton, Generalization in Reinforcement Learning: Successful examples using sparse coding, NIPS, 1996. [Paper] R-Learning (learning of relative values) Andrew Schwartz, A Reinforcement Learning Method for Maximizing Undiscounted Rewards, ICML, 1993. [Paper-Google Scholar] Function Approximation methods (Least-Sqaure Temporal Difference, Least-Sqaure Policy Iteration) Steven J. Bradtke, Andrew G. Barto, Linear Least-Squares Algorithms for Temporal Difference Learning, Machine Learning, 1996. [Paper] Michail G. Lagoudakis, Ronald Parr, Model-Free Least Squares Policy Iteration, NIPS, 2001. [Paper] [Code] Policy Search / Policy Gradient Richard Sutton, David McAllester, Satinder Singh, Yishay Mansour, Policy Gradient Methods for Reinforcement Learning with Function Approximation, NIPS, 1999. [Paper] Jan Peters, Sethu Vijayakumar, Stefan Schaal, Natural Actor-Critic, ECML, 2005. [Paper] Jens Kober, Jan Peters, Policy Search for Motor Primitives in Robotics, NIPS, 2009. [Paper] Jan Peters, Katharina Mulling, Yasemin Altun, Relative Entropy Policy Search, AAAI, 2010. [Paper] Freek Stulp, Olivier Sigaud, Path Integral Policy Improvement with Covariance Matrix Adaptation, ICML, 2012. [Paper] Nate Kohl, Peter Stone, Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion, ICRA, 2004. [Paper] Marc Deisenroth, Carl Rasmussen, PILCO: A Model-Based and Data-Efficient Approach to Policy Search, ICML, 2011. [Paper] Scott Kuindersma, Roderic Grupen, Andrew Barto, Learning Dynamic Arm Motions for Postural Recovery, Humanoids, 2011. [Paper] Hierarchical RL Richard Sutton, Doina Precup, Satinder Singh, Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning, Artificial Intelligence, 1999. [Paper] George Konidaris, Andrew Barto, Building Portable Options: Skill Transfer in Reinforcement Learning, IJCAI, 2007. [Paper] Deep Learning + Reinforcement Learning (A sample of recent works on DL+RL) V. Mnih, et. al., Human-level Control through Deep Reinforcement Learning, Nature, 2015. [Paper] Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard Lewis, Xiaoshi Wang, Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, NIPS, 2014. [Paper] Sergey Levine, Chelsea Finn, Trevor Darrel, Pieter Abbeel, End-to-End Training of Deep Visuomotor Policies. ArXiv, 16 Oct 2015. [ArXiv] Tom Schaul, John Quan, Ioannis Antonoglou, David Silver, Prioritized Experience Replay, ArXiv, 18 Nov 2015. [ArXiv] Hado van Hasselt, Arthur Guez, David Silver, Deep Reinforcement Learning with Double Q-Learning, ArXiv, 22 Sep 2015. [ArXiv]



Applications

Game Playing

Traditional Games Backgammon - "TD-Gammon" game play using TD(λ) (Tesauro, ACM 1995) [Paper] Chess - "KnightCap" program using TD(λ) (Baxter, arXiv 1999) [arXiv] Chess - Giraffe: Using deep reinforcement learning to play chess (Lai, arXiv 2015) [arXiv]

Computer Games Human-level Control through Deep Reinforcement Learning (Mnih, Nature 2015) [Paper] [Code] [Video] Flappy Bird Reinforcement Learning [Video] MarI/O - learning to play Mario with evolutionary reinforcement learning using artificial neural networks (Stanley, Evolutionary Computation 2002) [Paper][Video]



Robotics

Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion (Kohl, ICRA 2004) [Paper]

Robot Motor SKill Coordination with EM-based Reinforcement Learning (Kormushev, IROS 2010) [Paper] [Video]

Generalized Model Learning for Reinforcement Learning on a Humanoid Robot (Hester, ICRA 2010) [Paper] [Video]

Autonomous Skill Acquisition on a Mobile Manipulator (Konidaris, AAAI 2011) [Paper] [Video]

PILCO: A Model-Based and Data-Efficient Approach to Policy Search (Deisenroth, ICML 2011) [Paper]

Incremental Semantically Grounded Learning from Demonstration (Niekum, RSS 2013) [Paper]

Efficient Reinforcement Learning for Robots using Informative Simulated Priors (Cutler, ICRA 2015) [Paper] [Video]

Control

An Application of Reinforcement Learning to Aerobatic Helicopter Flight (Abbeel, NIPS 2006) [Paper] [Video]

Autonomous helicopter control using Reinforcement Learning Policy Search Methods (Bagnell, ICRA 2011) [Paper]

Operations Research

Scaling Average-reward Reinforcement Learning for Product Delivery (Proper, AAAI 2004) [Paper]

Cross Channel Optimized Marketing by Reinforcement Learning (Abe, KDD 2004) [Paper]

Human Computer Interaction

Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System (Singh, JAIR 2002) [Paper]

Tutorials / Websites

Online Demos