Deep Reinforcement Learning
The goal of this educational book is to keep track of the state-of-the-art in deep reinforcement learning. It starts with basics in reinforcement learning and deep learning to introduce the notations. It then covers different classes of deep RL methods, value-based or policy-based, model-free or model-based, etc. Later sections focus on more advanced topics.
This document is meant to stay work in progress forever, as new algorithms will be added as they are published. Feel free to comment, correct, suggest, pull request by writing to julien.vitay@gmail.com.
The book was used as the basis of the Deep RL course that I taught at the University of Chemnitz until 2025. You can find the slides there: https://julien-vitay.net/course-deeprl/.
Some figures are taken from the original publication (“Source:” in the caption). Their copyright stays to the respective authors, naturally. The rest is my own work and can be distributed, reproduced and modified under CC-BY-SA-NC 4.0.
Most of this book (everything up until model-based methods, included) has been written by hand by a human, old school. The “advanced topics” section has been generated in 2026 a posteriori by an AI agent based on the slides I produced for the course. I checked that it did not invent content. An agent also combed the human prose, fixed typos, and unified notations.
Except where otherwise noted, this work is licensed under a Creative Commons Attribution-Non Commercial-ShareAlike 4.0 International License.