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
- Codes for examples and exercises in Richard Sutton and Andrew Barto's Reinforcement Learning: An Introduction
- Simulation code for Reinforcement Learning Control ProblemsMATLAB Environment and GUI for Reinforcement Learning
- 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)
- [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.
- Marvin Minsky, Steps toward Artificial Intelligence, Proceedings of the IRE, 1961. [Paper]
-
Methods
- Dynamic Programming (DP):
- Christopher J. C. H. Watkins, Learning from Delayed Rewards, Ph.D. Thesis, Cambridge University, 1989. [Thesis]
- Monte Carlo:
- 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):
- 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)
- 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
- 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]
- Dynamic Programming (DP):
Applications
Game Playing
-
Traditional Games
-
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
- POMDP for Dummies
- Scholarpedia articles on:Repository with useful MATLAB Software, presentations, and demo videos
- 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