Changing not just analyzing: Control theory and reinforcement learning

Abstract

We give a short introduction to reinforcement learning. This includes basic concepts like Markov decision processes, policies, state-value and action-value functions, and the Bellman equation. We discuss solution methods like policy and value iteration methods, online methods like temporal-difference learning, and state fundamental convergence results. It turns out that RL addresses the problems from Chap. 2. This shows that, in principle, RL is a suitable instrument for solving all of these problems.