The aim of this seminar is to give an introduction to some of the mathematical ideas behind reinforcement learning. This includes stochastic optimisation and convergence analysis. The emphasis is on mathematical theory, not on developing and testing algorithms.

Objective

The aim of this seminar is to give an introduction to some of the mathematical ideas behind reinforcement learning. This includes stochastic optimisation and convergence analysis. The emphasis is on mathematical theory, not on developing and testing algorithms.

Content

The aim of this seminar is to give an introduction to some of the mathematical ideas behind reinforcement learning. This includes stochastic optimisation and convergence analysis. The emphasis is on mathematical theory, not on developing and testing algorithms.

The underlying textbook mostly works with stochastic control problems for discrete-time Markov chains with a finite state space. But for a proper understanding, students should be familiar with measure-theoretic probability theory as well as stochastic processes in discrete time, and in particular with the construction of Markov chains on the canonical path space via the Ionescu-Tulcea theorem.