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Awesome Reinforcement Learning Awesome

This page is no longer maintained.

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


Please feel free to pull requests

Table of Contents





  • 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)]


  • 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)


  • 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]


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]


  • 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]


  • 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]


Tutorials / Websites

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.
  • 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.

valuable Contributors👩‍💻👨‍💻 :


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