awesome-rl

(standard interface for RL) and

Python-Based Reinforcement learning, Artificial intelligence, and Neural network

Value-Function-Based Reinforcement Learning Framework for Education and Research

Machine learning framework for problems in Reinforcement Learning in python

Java based Reinforcement Learning framework

Platform Implementing Q-Learning and other RL algorithms

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 ( ) 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 simulator

Gold

A reinforcement learning library for Golang

Jumanji

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

[DeepMind x UCL]

[UCL] by David Silver

COMPMI22/COMPGI22 - Advanced Deep Learning and Reinforcement Learning

[UCL]

[Udacity (Georgia Tech.)]

[Stanford] by Andrew Ng

[UC Berkeley]

[UC Berkeley] by John Schulman and Pieter Abbeel

[CMU]

[MIT]

[Mutual Information]

Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (1st Edition, 1998)

Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction (2nd Edition, in progress, 2018)

Csaba Szepesvari, Algorithms for Reinforcement Learning

David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents

Dimitri P. Bertsekas and John N. Tsitsiklis, Neuro-Dynamic Programming

Mykel J. Kochenderfer, Decision Making Under Uncertainty: Theory and Application

Deep Reinforcement Learning in Action

REINFORCEMENT LEARNING AND OPTIMAL CONTROL Dimitri P. Bertsekas

Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore, Reinforcement Learning: A Survey (JAIR 1996)

S. S. Keerthi and B. Ravindran, A Tutorial Survey of Reinforcement Learning (Sadhana 1994)

Matthew E. Taylor, Peter Stone, Transfer Learning for Reinforcement Learning Domains: A Survey (JMLR 2009)

Jens Kober, J. Andrew Bagnell, Jan Peters, Reinforcement Learning in Robotics, A Survey (IJRR 2013)

Michael L. Littman, Reinforcement learning improves behaviour from evaluative feedback (Nature 2015)

Marc P. Deisenroth, Gerhard Neumann, Jan Peter, A Survey on Policy Search for Robotics, Foundations and Trends in Robotics (2014)

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath, A Brief Survey of Deep Reinforcement Learning (IEEE Signal Processing Magazine 2017)

Benjamin Recht, A Tour of Reinforcement Learning: The View from Continuous Control (Annu. Rev. Control Robot. Auton. Syst. 2019)

Marvin Minsky, Steps toward Artificial Intelligence, Proceedings of the IRE, 1961. (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. (earliest publication on temporal-difference (TD) learning rule)

Christopher J. C. H. Watkins, Learning from Delayed Rewards, Ph.D. Thesis, Cambridge University, 1989

Andrew Barto, Michael Duff, Monte Carlo Inversion and Reinforcement Learning, NIPS, 1994

Satinder P. Singh, Richard S. Sutton, Reinforcement Learning with Replacing Eligibility Traces, Machine Learning, 1996

Richard S. Sutton, Learning to predict by the methods of temporal differences. Machine Learning 3: 9-44, 1988

Chris Watkins, Learning from Delayed Rewards, Cambridge, 1989

G.A. Rummery, M. Niranjan, On-line Q-learning using connectionist systems, Technical Report, Cambridge Univ., 1994

Richard S. Sutton, Generalization in Reinforcement Learning: Successful examples using sparse coding, NIPS, 1996

Andrew Schwartz, A Reinforcement Learning Method for Maximizing Undiscounted Rewards, ICML, 1993

Steven J. Bradtke, Andrew G. Barto, Linear Least-Squares Algorithms for Temporal Difference Learning, Machine Learning, 1996

Michail G. Lagoudakis, Ronald Parr, Model-Free Least Squares Policy Iteration, NIPS, 2001

Richard Sutton, David McAllester, Satinder Singh, Yishay Mansour, Policy Gradient Methods for Reinforcement Learning with Function Approximation, NIPS, 1999

Jan Peters, Sethu Vijayakumar, Stefan Schaal, Natural Actor-Critic, ECML, 2005

Jens Kober, Jan Peters, Policy Search for Motor Primitives in Robotics, NIPS, 2009

Jan Peters, Katharina Mulling, Yasemin Altun, Relative Entropy Policy Search, AAAI, 2010

Freek Stulp, Olivier Sigaud, Path Integral Policy Improvement with Covariance Matrix Adaptation, ICML, 2012

Nate Kohl, Peter Stone, Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion, ICRA, 2004

Marc Deisenroth, Carl Rasmussen, PILCO: A Model-Based and Data-Efficient Approach to Policy Search, ICML, 2011

Scott Kuindersma, Roderic Grupen, Andrew Barto, Learning Dynamic Arm Motions for Postural Recovery, Humanoids, 2011

Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik, Dorian Goepp, Vassilis Vassiliades, Jean-Baptiste Mouret, Black-Box Data-efficient Policy Search for Robotics, IROS, 2017. [ ]

Richard Sutton, Doina Precup, Satinder Singh, Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning, Artificial Intelligence, 1999

George Konidaris, Andrew Barto, Building Portable Options: Skill Transfer in Reinforcement Learning, IJCAI, 2007

V. Mnih, et. al., Human-level Control through Deep Reinforcement Learning, Nature, 2015

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

Sergey Levine, Chelsea Finn, Trevor Darrel, Pieter Abbeel, End-to-End Training of Deep Visuomotor Policies. ArXiv, 16 Oct 2015

Tom Schaul, John Quan, Ioannis Antonoglou, David Silver, Prioritized Experience Replay, ArXiv, 18 Nov 2015

Hado van Hasselt, Arthur Guez, David Silver, Deep Reinforcement Learning with Double Q-Learning, ArXiv, 22 Sep 2015

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

Backgammon - Gerald Tesauro, "TD-Gammon" game play using TD(λ) (ACM 1995)

Chess - Jonathan Baxter, Andrew Tridgell and Lex Weaver, "KnightCap" program using TD(λ) (1999)

Chess - Matthew Lai, Giraffe: Using deep reinforcement learning to play chess (2015)

Atari 2600 Games - Volodymyr Mnih, Koray Kavukcuoglu, David Silver et al., Human-level Control through Deep Reinforcement Learning (Nature 2015)

Flappy Bird Reinforcement Learning

Flappy Bird - Sarvagya Vaish,

Mario - Kenneth O. Stanley and Risto Miikkulainen, MarI/O - learning to play Mario with evolutionary reinforcement learning using artificial neural networks (Evolutionary Computation 2002)

StarCraft II - Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki et al., Grandmaster level in StarCraft II using multi-agent reinforcement learning (Nature 2019)

Nate Kohl and Peter Stone, Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion (ICRA 2004)

Petar Kormushev, Sylvain Calinon and Darwin G. Caldwel, Robot Motor SKill Coordination with EM-based Reinforcement Learning (IROS 2010)

Todd Hester, Michael Quinlan, and Peter Stone, Generalized Model Learning for Reinforcement Learning on a Humanoid Robot (ICRA 2010)

George Konidaris, Scott Kuindersma, Roderic Grupen and Andrew Barto, Autonomous Skill Acquisition on a Mobile Manipulator (AAAI 2011)

Marc Peter Deisenroth and Carl Edward Rasmussen,PILCO: A Model-Based and Data-Efficient Approach to Policy Search (ICML 2011)

Scott Niekum, Sachin Chitta, Bhaskara Marthi, et al., Incremental Semantically Grounded Learning from Demonstration (RSS 2013)

Mark Cutler and Jonathan P. How, Efficient Reinforcement Learning for Robots using Informative Simulated Priors (ICRA 2015)

Antoine Cully, Jeff Clune, Danesh Tarapore and Jean-Baptiste Mouret, Robots that can adapt like animals (Nature 2015) [ ] [ ] [ ]

Konstantinos Chatzilygeroudis, Roberto Rama, Rituraj Kaushik et al, Black-Box Data-efficient Policy Search for Robotics (IROS 2017) [ ] [ ] [ ]

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)

Pieter Abbeel, Adam Coates, et al., An Application of Reinforcement Learning to Aerobatic Helicopter Flight (NIPS 2006)

J. Andrew Bagnell and Jeff G. Schneider, Autonomous helicopter control using Reinforcement Learning Policy Search Methods (ICRA 2001)

Scott Proper and Prasad Tadepalli, Scaling Average-reward Reinforcement Learning for Product Delivery (AAAI 2004)

Naoki Abe, Naval Verma et al., Cross Channel Optimized Marketing by Reinforcement Learning (KDD 2004)

Bernd Waschneck, Andre Reichstaller, Lenz Belzner et al., Deep reinforcement learning for semiconductor production scheduling (ASMC 2018)

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

Python Code

(2nd Edition)

(1st Edition)

(standard interface for RL) and

Python-Based Reinforcement learning, Artificial intelligence, and Neural network

Value-Function-Based Reinforcement Learning Framework for Education and Research

Machine learning framework for problems in Reinforcement Learning in python

Java based Reinforcement Learning framework

Platform Implementing Q-Learning and other RL algorithms

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 ( ) 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 simulator

Jumanji

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

Mance Harmon and Stephanie Harmon,

C. Igel, M.A. Riedmiller, et al., Reinforcement Learning in a Nutshell, ESANN, 2007

UNSW -

Repository with useful

UC Berkeley - CS 294: Deep Reinforcement Learning, Fall 2015 (John Schulman, Pieter Abbeel)

by Travis DeWolf

Atari 2600 games environment for developing AI agents

by Andrej Karpathy

by Arthur Juliani

Practical_RL

github-based course in reinforcement learning in the wild (lectures, coding labs, projects)

Katja Hofmann's talk at NeurIPS '19 -

A summary of some important concepts and algorithms in RL

A deep Q learning demonstration using ConvNetJS

Deep Q-Learning with Tensor Flow

A deep Q learning demonstration using Google Tensorflow

A reinforcement learning demo using reinforcejs by Andrej Karpathy

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

Brown-UMBC Reinforcement Learning and Planning, a library written in Java

MAgent

A Platform for Many-agent Reinforcement Learning

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

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