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
Surveys
Papers / Thesis
ApplicationsTutorials / Websites
Game Playing
Robotics
Control
Operations Research
Human Computer Interaction
Online Demos
Codes
Codes for examples and exercises in Richard Sutton and Andrew Barto's Reinforcement Learning: An Introduction
MATLAB Code
C/Lisp Code
Book
Simulation code for Reinforcement Learning Control ProblemsMATLAB Environment and GUI for Reinforcement Learning
Pole-Cart Problem
Q-learning Controller
Reinforcement Learning Repository - University of Massachusetts, Amherst
Brown-UMBC Reinforcement Learning and Planning Library (Java)
Reinforcement Learning in R (MDP, Value Iteration)
Reinforcement Learning Environment in Python and MATLAB
RL-Glue (standard interface for RL) and RL-Glue Library
PyBrain Library - Python-Based Reinforcement learning, Artificial intelligence, and Neural network
RLPy Framework - Value-Function-Based Reinforcement Learning Framework for Education and Research
Maja - Machine learning framework for problems in Reinforcement Learning in python
TeachingBox - Java based Reinforcement Learning framework
Policy Gradient Reinforcement Learning Toolbox for MATLAB
PIQLE - Platform Implementing Q-LEarning and other RL algorithms
BeliefBox - Bayesian reinforcement learning library and toolkit
Deep Q-Learning with Tensor Flow - A deep Q learning demonstration using Google Tensorflow
Theory
Lectures
[UCL] COMPM050/COMPGI13 Reinforcement Learning by David Silver
[UC Berkeley] CS188 Artificial Intelligence by Pieter Abbeel[Udacity (Georgia Tech.)] Machine Learning 3: Reinforcement Learning (CS7641)
Lecture 8: Markov Decision Processes 1
Lecture 9: Markov Decision Processes 2
Lecture 10: Reinforcement Learning 1
Lecture 11: Reinforcement Learning 2
[Stanford] CS229 Machine Learning - Lecture 16: Reinforcement Learning by Andrew Ng
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]
Matthew E. Taylor, Peter Stone, Transfer Learning for Reinforcement Learning Domains: A Survey, JMLR, 2009. [Paper]
Jens Kober, J. Andrew Bagnell, Jan Peters, Reinforcement Learning in Robotics, A Survey, IJRR, 2013. [Paper]
Michael L. Littman, "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]
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu, Asynchronous Methods for Deep Reinforcement Learning, ArXiv, 4 Feb 2016.[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
Mance Harmon and Stephanie Harmon, Reinforcement Learning: A Tutorial
Short introduction to some Reinforcement Learning algorithms
C. Igel, M.A. Riedmiller, et al., Reinforcement Learning in a Nutshell, ESANN, 2007. [Paper]
UNSW - Reinforcement LearningROS Reinforcement Learning Tutorial
Introduction
TD-Learning
Q-Learning and SARSA
Applet for "Cat and Mouse" Game
POMDP for Dummies
Scholarpedia articles on:Repository with useful MATLAB Software, presentations, and demo videos
Reinforcement Learning
Temporal Difference Learning
Bibliography on Reinforcement Learning
UC Berkeley - CS 294: Deep Reinforcement Learning, Fall 2015 (John Schulman, Pieter Abbeel) [Class Website]
Blog posts on Reinforcement Learning, Parts 1-4 by Travis DeWolf
The Arcade Learning Environment - Atari 2600 games environment for developing AI agents
Deep Reinforcement Learning: Pong from Pixels by Andrej Karpathy
Demystifying Deep Reinforcement Learning
Online Demos
Real-world demonstrations of Reinforcement Learning
Deep Q-Learning Demo - A deep Q learning demonstration using ConvNetJS
Deep Q-Learning with Tensor Flow - A deep Q learning demonstration using Google Tensorflow
Reinforcement Learning Demo - A reinforcement learning demo using reinforcejs by Andrej Karpathy
