Reinforcement Learning and Optimal Control

Reinforcement Learning (RL), one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment.

The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable.

We rely more on intuitive explanations and less on proof-based insights. Still we provide a rigorous short account of the theory of finite and infinite horizon dynamic programming, and some basic approximation methods, in an appendix. For this we require a modest mathematical background: calculus, elementary probability, and a minimal use of matrix-vector algebra.