Best Books and Courses to Learn about Reinforcement Learning in 2022

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Introduction

Deep learning is an approach to designing, training, and building machine learning models that became extremely popular lately. Arguably, deep learning’s popularity is mainly due to three reasons. Firstly, deep learning model have performed greatly in many tasks in areas such natural language processing, computer vision, and speech recognition in comparison to alternative approaches such as tree-based learning methods like decision trees and support vector machines. Secondly, deep learning is versatile, which means it can be used to solve an extensive range of problems. Thirdly, deep learning, to a large extent, has removed the need for feature engineering, which was an enduring challenge in itself in building machine learning models.

Reinforcement learning is an area of machine learning that the main goal is to train an agent that aim to maximize a commutative reward by taking actions in an environment. The main application of reinforcement learning is to create an agent that that can solve a problem or perform a task that previously was solved or performed by a human being. As an example of such a task, reinforcement learning was used to train an agent that plays a video game like Super Mario World.

Deep reinforcement learning comes about when deep learning is used to approximate different components of a reinforcement learning-based system, such as the reward function. Deep reinforcement learning is a growing area for researchers and practitioners, likewise. This article aims to present a compact list of high-quality resources including books and online course to help anybody curious about reinforcement learning, in general, and deep reinforcement learning, in particular, get started quickly. Moreover, most of the books enlisted are available as downloadable PDFs. And, the code for the examples shown in the books are mostly available.

At any rate, I hope these resources will help you in your journey to learn more about (deep) reinforcement learning concepts and tools.

Books

1. Reinforcement Learning, Second Edition, An Introduction by By Richard S. Sutton and Andrew G. Barto
2. Algorithms for Decision Making by By Mykel J. Kochenderfer, Tim A. Wheeler and Kyle H. Wray
3. Bandit Algorithms by Tor Lattimore and Csaba Szepesvári
4. Reinforcement Learning: Theory and Algorithms by Alekh Agarwal, Nan Jiang, Sham M. Kakade and Wen Sun

Courses

1. Reinforcement Learning Lecture Series 2021 by DeepMind x UCL
2. CMPUT 653: Theoretical Foundations of Reinforcement Learning by University of Alberta
3. CS 285: Deep Reinforcement Learning by UC Berkeley
4. The Hugging Face Deep Reinforcement Learning Class
5. Introduction to Reinforcement Learning with David Silver
6. CS234: Reinforcement Learning Winter 2019 by Stanford University
7. Foundations of Deep RL — 6-lecture series by Pieter Abbeel

Other resources

1. Reinforcement Learning Discord Wiki
2. Reinforcement Learning Summer School by Vrije Universiteit Amsterdam

Acknowledgement

The resources introduced in this article are from this blog post by Yanzhe Bekkemoen.