Reinforcement Learning

Presentation

The course presents the theoretical foundations of reinforcement learning as well as the principles of the most common algorithms. Through practical work, these elements will be extended to more complex situations, making it possible to introduce the most recent algorithms that have, for example, enabled computers to master the game of Go.

Learning outcomes

• To understand the theoretical foundations of reinforcement learning.
• To implement these methods in a way that is adapted to the problems to be solved.
• To sharpen your critical thinking skills.

Syllabus

Reinforcement learning is introduced using the formal framework of the Markov Decision Processes. After having shown the existence and uniqueness of a solution in the form of the value function, we will discuss the classical algorithms used to calculate this function. We will then see how approximate methods (linear approximation, monte carlo estimation, bandits, deep learning) can be used to tackle more complex contexts.