# Reinforcement Learning

Graduate course, *Université de Lille, Computer science*, 2021

The main reference for this course may be found on Philippe Preux’s website.

## Practical session 1

### Randomness with computers

## Practical session 2

### Bellman equations & planning

## Evaluation

Below is a checklist for students to use and revise the concepts that were tackled in class.

### Control theory

- Definition of Markov Decision Process
- Markov property
- Discount factor / Discounted reward
- Discounted value, Finite time horizon value
- Bellman operator, Bellman optimal operator
- Dynamic Programming principle
- Policy Evaluation: Direct computation, Iteration, Monte-Carlo
- Value Iteration
- Policy Iteration