Introduction

Mathematical Introduction to Reinforcement Learning

Reinforcement learning (RL) addresses problems of sequential decision making and stochastic control and is strongly connected to dynamic programming and Markov decision processes. In the last decades it has gained importance and has become major field of study in machine learning and artificial intelligence. Researchers from a variety of scientific fields that reach from cognitive sciences, neurology and psychology to computer science, physics and mathematics, have developed algorithms and techniques with impressive applications as well as mathematical foundations.

Reinforcement learning is based on the simple idea of learning by trial and error while interacting with an environment. At each step the agent performs an action and receives a reward depending on the starting state, the action and the environment. The agent learns to choose actions that maximize the sum of all rewards in the long run. The resulting choice of an action for each state is called a policy. Finding optimal policies is the primary objective of reinforcement learning.

In this workshop, we give an introduction into the mathematical aspects of RL, its current methods, applications and general scope. The workshop will make use of simulators and interactive applets to introduce aspects of RL in a hands-on way.