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Awesome Reinforcement Learning

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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

Contributing

Please feel free to pull requests

Table of Contents

• Theory
  • Lectures
  • Books
  • Surveys
  • Papers / Thesis
• Applications
  • Game Playing
  • Robotics
  • Control
  • Operations Research
  • Human Computer Interaction
• Codes
• 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 (BROKEN LINK)
  • C/Lisp Code
  • Julia Code
  • Book
  • Exercise Solutions
• 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 TensorFlow - 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.
• Reinforcement Learning Examples by RLCode - A Collection of minimal and clean reinforcement learning examples
• OpenAI Baselines - Well tested implementations (and results) of reinforcement learning algorithms from OpenAI
• PyTorch Deep RL - Popular deep RL algorithm implementations with PyTorch
• ChainerRL - Popular deep RL algorithm implementations with Chainer
• Black-DROPS - Modular and generic code for the model-based policy search Black-DROPS algorithm (IROS 2017 paper) and easy integration with the DART simulator
• Gold - A reinforcement learning library for Golang.
• Jumanji - A Suite of Industry-Driven Hardware-Accelerated RL Environments written in JAX.

Theory

Lectures

• [DeepMind x UCL] Reinforcement Learning Lecture Series 2021
• [UCL] COMPM050/COMPGI13 Reinforcement Learning by David Silver
• [UCL] COMPMI22/COMPGI22 - Advanced Deep Learning and Reinforcement Learning
• [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.)] CS7642 Reinforcement Learning
• [Stanford] CS229 Machine Learning - Lecture 16: Reinforcement Learning by Andrew Ng
• [UC Berkeley] Deep RL Bootcamp
• [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
• [Siraj Raval]: Introduction to AI for Video Games (Reinforcement Learning Video Series)
  • Introduction to AI for video games
  • Monte Carlo Prediction
  • Q learning explained
  • Solving the basic game of Pong
  • Actor Critic Algorithms
  • War Robots
• [Mutual Information] Reinforcement Learning Fundamentals
  • Reinforcement Learning: A Six Part Series
  • The Bellman Equations, Dynamic Programming, and Generalized Policy Iteration
  • Monte Carlo And Off-Policy Methods
  • TD Learning, Sarsa, and Q-Learning

Books

• Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (1st Edition, 1998) [Book] [Code]
• Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (2nd Edition, in progress, 2018) [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)]
• Deep Reinforcement Learning in Action [Book(Manning)]
• REINFORCEMENT LEARNING AND OPTIMAL CONTROL Dimitri P. Bertsekas BOOK, VIDEOLECTURES, AND COURSE MATERIAL, 2019

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 2015) [Paper]
• Marc P. Deisenroth, Gerhard Neumann, Jan Peter, A Survey on Policy Search for Robotics, Foundations and Trends in Robotics (2014) [Book]
• Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath, A Brief Survey of Deep Reinforcement Learning (IEEE Signal Processing Magazine 2017) [DOI] [Paper]
• Benjamin Recht, A Tour of Reinforcement Learning: The View from Continuous Control (Annu. Rev. Control Robot. Auton. Syst. 2019) [DOI]

Papers / Thesis

Foundational Papers

• Marvin Minsky, Steps toward Artificial Intelligence, Proceedings of the IRE, 1961. [DOI] [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. [DOI] [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-Square Temporal Difference, Least-Square 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]
  • Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik, Dorian Goepp, Vassilis Vassiliades, Jean-Baptiste Mouret, Black-Box Data-efficient Policy Search for Robotics, IROS, 2017. [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 - Gerald Tesauro, "TD-Gammon" game play using TD(λ) (ACM 1995) [Paper]
• Chess - Jonathan Baxter, Andrew Tridgell and Lex Weaver, "KnightCap" program using TD(λ) (1999) [arXiv]
• Chess - Matthew Lai, Giraffe: Using deep reinforcement learning to play chess (2015) [arXiv]

Computer Games

• Atari 2600 Games - Volodymyr Mnih, Koray Kavukcuoglu, David Silver et al., Human-level Control through Deep Reinforcement Learning (Nature 2015) [DOI] [Paper] [Code] [Video]
• Flappy Bird - Sarvagya Vaish, Flappy Bird Reinforcement Learning [Video]
• Mario - Kenneth O. Stanley and Risto Miikkulainen, MarI/O - learning to play Mario with evolutionary reinforcement learning using artificial neural networks (Evolutionary Computation 2002) [Paper] [Video]
• StarCraft II - Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki et al., Grandmaster level in StarCraft II using multi-agent reinforcement learning (Nature 2019) [DOI] [Paper] [Video]

Robotics

• Nate Kohl and Peter Stone, Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion (ICRA 2004) [Paper]
• Petar Kormushev, Sylvain Calinon and Darwin G. Caldwel, Robot Motor SKill Coordination with EM-based Reinforcement Learning (IROS 2010) [Paper] [Video]
• Todd Hester, Michael Quinlan, and Peter Stone, Generalized Model Learning for Reinforcement Learning on a Humanoid Robot (ICRA 2010) [Paper] [Video]
• George Konidaris, Scott Kuindersma, Roderic Grupen and Andrew Barto, Autonomous Skill Acquisition on a Mobile Manipulator (AAAI 2011) [Paper] [Video]
• Marc Peter Deisenroth and Carl Edward Rasmussen,PILCO: A Model-Based and Data-Efficient Approach to Policy Search (ICML 2011) [Paper]
• Scott Niekum, Sachin Chitta, Bhaskara Marthi, et al., Incremental Semantically Grounded Learning from Demonstration (RSS 2013) [Paper]
• Mark Cutler and Jonathan P. How, Efficient Reinforcement Learning for Robots using Informative Simulated Priors (ICRA 2015) [Paper] [Video]
• Antoine Cully, Jeff Clune, Danesh Tarapore and Jean-Baptiste Mouret, Robots that can adapt like animals (Nature 2015) [ArXiv] [Video] [Code]
• Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik et al, Black-Box Data-efficient Policy Search for Robotics (IROS 2017) [ArXiv] [Video] [Code]
• P. Travis Jardine, Michael Kogan, Sidney N. Givigi and Shahram Yousefi, Adaptive predictive control of a differential drive robot tuned with reinforcement learning (Int J Adapt Control Signal Process 2019) [DOI]

Control

• Pieter Abbeel, Adam Coates, et al., An Application of Reinforcement Learning to Aerobatic Helicopter Flight (NIPS 2006) [Paper] [Video]
• J. Andrew Bagnell and Jeff G. Schneider, Autonomous helicopter control using Reinforcement Learning Policy Search Methods (ICRA 2001) [Paper]

Operations Research

• Scott Proper and Prasad Tadepalli, Scaling Average-reward Reinforcement Learning for Product Delivery (AAAI 2004) [Paper]
• Naoki Abe, Naval Verma et al., Cross Channel Optimized Marketing by Reinforcement Learning (KDD 2004) [Paper]
• Bernd Waschneck, Andre Reichstaller, Lenz Belzner et al., Deep reinforcement learning for semiconductor production scheduling (ASMC 2018) [DOI] [Paper]

Human Computer Interaction

• Satinder Singh, Diane Litman et al., Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System (JAIR 2002) [Paper]

Codes

• Codes for examples and exercises in Richard Sutton and Andrew Barto's Book Reinforcement Learning: An Introduction

  • Python Code (2nd Edition)
  • MATLAB Code (1st Edition)

• 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 TensorFlow - 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.

• Reinforcement Learning Examples by RLCode - A Collection of minimal and clean reinforcement learning examples

• OpenAI Baselines - Well tested implementations (and results) of reinforcement learning algorithms from OpenAI

• PyTorch Deep RL - Popular deep RL algorithm implementations with PyTorch

• ChainerRL - Popular deep RL algorithm implementations with Chainer

• Black-DROPS - Modular and generic code for the model-based policy search Black-DROPS algorithm (IROS 2017 paper) and easy integration with the DART simulator

• Jumanji - A Suite of Industry-Driven Hardware-Accelerated RL Environments written in JAX.

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
• Practical_RL - github-based course in reinforcement learning in the wild (lectures, coding labs, projects)
• RLenv.directory: Explore and find new reinforcement learning environments.
• Katja Hofmann's talk at NeurIPS '19 - RL: Past, Present and Future Perspectives
• How to Structure, Organize, Track and Manage Reinforcement Learning (RL) Projects
• Reinforcement Learning Cheat Sheet - A summary of some important concepts and algorithms in RL

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 measuring 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
• Retro Learning Environment - An AI platform for reinforcement learning based on video game emulators. Currently supports SNES and Sega Genesis. Compatible with OpenAI gym.
• 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
• garage - A framework for reproducible reinformcement learning research, fully compatible with OpenAI Gym and DeepMind Control Suite (successor to rllab)
• TensorForce - Practical deep reinforcement learning on TensorFlow with Gitter support and OpenAI Gym/Universe/DeepMind Lab integration.
• tf-TRFL - A library built on top of TensorFlow that exposes several useful building blocks for implementing Reinforcement Learning agents.
• OpenAI lab - An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
• keras-rl - State-of-the art deep reinforcement learning algorithms in Keras designed for compatibility with OpenAI.
• BURLAP - Brown-UMBC Reinforcement Learning and Planning, a library written in Java
• MAgent - A Platform for Many-agent Reinforcement Learning.
• Ray RLlib - Ray RLlib is a reinforcement learning library that aims to provide both performance and composability.
• SLM Lab - A research framework for Deep Reinforcement Learning using Unity, OpenAI Gym, PyTorch, Tensorflow.
• Unity ML Agents - Create reinforcement learning environments using the Unity Editor
• Intel Coach - Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms.
• Microsoft AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research.
• DI-engine - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
• Jumanji - A Suite of Industry-Driven Hardware-Accelerated RL Environments written in JAX.

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