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
• Applications
  • Game Playing
  • Robotics
  • Control
  • Operations Research
  • Human Computer Interaction
• Tutorials / Websites
• Online Demos
• Open Source Reinforcement Learning Platforms

Codes

• Codes for examples and exercises in Richard Sutton and Andrew Barto's Reinforcement Learning: An Introduction
• Python Code
• MATLAB Code
• C/Lisp Code
• Book
• Simulation code for Reinforcement Learning Control Problems
• Pole-Cart Problem
• Q-learning Controller
• MATLAB 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
• Atari - Deep Q-networks and asynchronous agents in Torch
• AgentNet - A python library for deep reinforcement learning and custom recurrent networks using Theano+Lasagne.

Theory

Lectures

• [UCL] COMPM050/COMPGI13 Reinforcement Learning by David Silver
• [UC Berkeley] CS188 Artificial Intelligence by Pieter Abbeel
  • Lecture 8: Markov Decision Processes 1
  • Lecture 9: Markov Decision Processes 2
  • Lecture 10: Reinforcement Learning 1
  • Lecture 11: Reinforcement Learning 2
• [Udacity (Georgia Tech.)] Machine Learning 3: Reinforcement Learning (CS7641)
• [Stanford] CS229 Machine Learning - Lecture 16: Reinforcement Learning by Andrew Ng
• [UC Berkeley] CS294 Deep Reinforcement Learning by John Schulman and Pieter Abbeel
• [CMU] 10703: Deep Reinforcement Learning and Control, Spring 2017
• [MIT] 6.S094: Deep Learning for Self-Driving Cars
  • Lecture 2: Deep Reinforcement Learning for Motion Planning

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
• C. Igel, M.A. Riedmiller, et al., Reinforcement Learning in a Nutshell, ESANN, 2007. [Paper]
• UNSW - Reinforcement Learning
• Introduction
• TD-Learning
• Q-Learning and SARSA
• Applet for "Cat and Mouse" Game
• ROS Reinforcement Learning Tutorial
• POMDP for Dummies
• Scholarpedia articles on:
• Reinforcement Learning
• Temporal Difference Learning
• 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
• Let’s make a DQN
• Simple Reinforcement Learning with Tensorflow, Parts 0-8 by Arthur Juliani

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

Open Source Reinforcement Learning Platforms

• OpenAI gym - A toolkit for developing and comparing reinforcement learning algorithms
• OpenAI universe - A software platform for measruing and training an AI's general intelligence across the world's supply of games, websites and other applications
• DeepMind Lab - A customisable 3D platform for agent-based AI research
• Project Malmo - A platform for Artificial Intelligence experimentation and research built on top of Minecraft by Microsoft
• ViZDoom - Doom-based AI research platform for reinforcement learning from raw visual information
• torch-twrl - A package that enables reinforcement learning in Torch by Twitter
• UETorch - A Torch plugin for Unreal Engine 4 by Facebook
• TorchCraft - Connecting Torch to StarCraft
• rllab - A framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym
• TensorForce - Practical deep reinforcement learning on TensorFlow with Gitter support and OpenAI Gym/Universe/DeepMind Lab integration.