UE Reinforcement Learning

Diplômes intégrant cet élément pédagogique :


Reinforcement learning is an area of machine learning in which an agent interacts repeatedly with an environment in order to maximize their cumulative reward. Compared to the classical supervised or unsupervised learning frameworks, here we are typically interested in problem in which an agent takes decision and learn at the same time, a paradigm that is also known as online learning (in which a typical tradeoff is the exploration versus exploitation dilemma). The application of reinforcement learning spans many areas of artificial intelligence. For instance, driving a car or designing a computer that plays the game of go can be achieved by reinforcement learning techniques.

The goal of this course will be to provide an overview of the main tools used to apprehends these problems. This course will have a strong theoretical component. We will cover the basics of online optimization (multi-armed bandits algorithms, regret minimization), and of Markov decision processes, Bellman’s optimality principle and basic learning algorithms for Markov processes. Throughout the course, we will focus on the mathematical and the algorithmic aspects of the theory. We will present implementation tutorials for the course’s algorithmic content.

Informations complémentaires

Langue(s) : Anglais