Need help with awesome-rl?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.
aikorea

Description

Reinforcement learning resources curated

6.7K Stars 1.6K Forks 176 Commits 17 Opened issues

Services available

Need anything else?

Awesome Reinforcement Learning Awesome

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 (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)]

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

## 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

Online Demos

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.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.