Reinforcement Learning

List of resources about reinforcement learning organized into two sections; Textbooks & Resources, and Papers. I update the list on an ongoing basis. See Neural Networks for general resources on deep learning.

Textbooks & Resources

  • Deep Reinforcement Learning: Good 90 minute video overview by John Schulman. Assumes some knowledge of neural networks. If you are not familiar with neural networks, then start with Sutton and Barto’s book.
  • Reinforcement Learning: An Introduction: Classic textbook by Sutton and Barto. A good place to go next after watching John Schulman’s talk. Link is to the in progress 2nd edition. First edition published in 1998 is available here.
  • David Silver’s Reinforcement Learning Course: David Silver is a fantastic teacher. The canonical source for reinforcement learning.
  • The Open AI Lab: An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras. The Lab handles the basic RL environment and algorithm setups, provides implementations of standard components for deep reinforcement learning algorithms, such Q-Learning or Sarsa, and provides automated plots and analytics for testing new RL algorithms. Makes it easier both for beginners to get started with RL and for experienced practitioners to develop and compare new algorithms
  • Denny Britz’ implementations of the major reinforcement learning algorithms.
  • The OpenAI gym: Open source project which provides a simple interface to various reinforcement learning problems. This project deals with the simulating environments so that you can focus on implementing and testing algorithms.
  • Unity ML Agents: SDK for designing environments, games, and simulations where agents can be trained using reinforcement learning, evolutionary algorithms, or any other machine learning method.


I’m always looking to learn more. If you have any comments or suggestions, please send me an email on contact [at] learningmachinelearning [dot] org