Awesome Reinforcement Learning
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
- Codes for examples and exercises in Richard Sutton and Andrew Barto's Reinforcement Learning: An Introduction
- Simulation code for Reinforcement Learning Control Problems
- 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
- Maja - Machine learning framework for problems in Reinforcement Learning in python
- TeachingBox - Java based Reinforcement Learning framework
- Implementation of RL algorithms in Python/C++
- Policy Gradient Reinforcement Learning Toolbox for MATLAB
- PIQLE - Platform Implementing Q-LEarning and other RL algorithms
Theory
Lectures
- [UCL] COMPM050/COMPGI13 Reinforcement Learning by David Silver
- [UC Berkeley] CS188 Artificial Intelligence by Pieter Abbeel
- [Udacity (Georgia Tech.)] Machine Learning 3: Reinforcement Learning (CS7641)
- [Stanford] CS229 Machine Learning - Lecture 16: Reinforcement Learning by Andrew Ng
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]
- Jens Kober, J. Andrew Bagnell, Jan Peters, Reinforcement Learning in Robotics, A Survey, IJRR, 2013. [Paper]
- Littman, Michael L. "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.
- Marvin Minsky, Steps toward Artificial Intelligence, Proceedings of the IRE, 1961.[Paper]
-
Solution Methods
- Dynamic Programming (DP):
- Christopher J. C. H. Watkins, Learning from Delayed Rewards, Ph.D. Thesis, Cambridge University, 1989. [Thesis]
- Monte Carlo:
- 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):
- 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)
- Policy Search (in application to Robotics)
- 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]
- Jan Peters, Sethu Vijayakumar, Stefan Schaal, Natural Actor-Critic, ECML, 2005.[Paper]
- Scott Kuindersma, Roderic Grupen, Andrew Barto, Learning Dynamic Arm Motions for Postural Recovery, Humanoids, 2011. [Paper]
- Hierarchical RL
- Dynamic Programming (DP):
Applications
Game Playing
-
Traditional Games
- Backgammon - "TD-Gammon" game play using TD(λ) (ACM 1995) [Paper]
- Chess - "KnightCap" program using TD(λ) [Paper-arXiv]
- Chess - Giraffe: Using deep reinforcement learning to play chess [Paper-arXiv]
-
Computer Games
Robotics
- Reinforcement Learning for Humanoid Robotics (ICHR 2003) [Paper]
- Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion (ICRA 2004)[Paper]
- Robot Motor SKill Coordination with EM-based Reinforcement Learning (IROS 2010) [Paper][Video]
- Generalized Model Learning for Reinforcement Learning on a Humanoid Robot (ICRA 2010)[Paper] [Video]
- Autonomous Skill Acquisition on a Mobile Manipulator (AAAI 2011) [Paper] [Video]
- PILCO: A Model-Based and Data-Efficient Approach to Policy Search (ICML 2011) [Paper]
- Incremental Semantically Grounded Learning from Demonstration (RSS 2013) [Paper]
- Efficient Reinforcement Learning for Robots using Informative Simulated Priors (ICRA 2015)[Paper] [Video]
Control
- An Application of Reinforcement Learning to Aerobatic Helicopter Flight (NIPS 2006) [Paper][Video]
- Autonomous helicopter control using Reinforcement Learning Policy Search Methods (ICRA 2011) [Paper]
Operations Research
- Scaling Average-reward Reinforcement Learning for Product Delivery (AAAI 2004) [Paper]
- Cross Channel Optimized Marketing by Reinforcement Learning (KDD 2004) [Paper]
Human Computer Interaction
- Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System (JAIR 2002) [Paper]
Tutorials / Websites
- Mance Harmon and Stephanie Harmon, Reinforcement Learning: A Tutorial
- Short introduction to some Reinforcement Learning algorithms
- C. Igel, M.A. Riedmiller, et al., Reinforcement Learning in a Nutshell, ESANN, 2007. [Paper]
- UNSW - Reinforcement Learning
- ROS Reinforcement Learning Tutorial
- POMDP for Dummies
- Scholarpedia articles on:
- Repository with useful MATLAB Software, presentations, and demo videos
- Bibliography on Reinforcement Learning