# Deep Reinforcement Learning

# Overview

This website contains the materials for the module **Deep Reinforcement Learning** taught by Dr. Julien Vitay at the Technische Universität Chemnitz, Faculty of Computer Science, Professorship for Artificial Intelligence.

Each section/lecture is accompanied by a set of videos, the slides and a written version of the content. The videos are integrated in the lecture notes, but you can also access the complete playlist on Youtube.

Exercises are provided in the form of Jupyter notebooks, allowing to implement in Python at your own pace the algorithms seen in the lectures and to learn to use reinforcement learning libraries such as `gym`

. A notebook to work on (locally or on Colab) and the solution are available in the Exercises section.

## Syllabus

### Lectures

**Introduction****Tabular RL****Model-free RL****Model-based RL****Outlook**

### Exercises

Notebooks and videos are in the List of Exercises. Below are links to the rendered solutions.

## Recommended readings

(Sutton and Barto, 2017) Richard Sutton and Andrew Barto (2017). Reinforcement Learning: An Introduction. MIT press. http://incompleteideas.net/book/the-book-2nd.html

CS294 course of Sergey Levine at Berkeley.

http://rll.berkeley.edu/deeprlcourse/

- Reinforcement Learning course by David Silver at UCL.