Control Systems and Reinforcement Learning

Control Systems and Reinforcement Learning

Published by Cambridge University Press  CS&RL

They have kindly allowed me to provide free of charge a pre-publication draft.

I will maintain here a list of resources, links to discoveries, and errata as I find them.


The organization is unique:

  • Part I: Fundamentals Without Noise
  • Part II: Reinforcement Learning and Stochastic Control

The first half of the book is accessible to a reader without any background in probabilistic methods.  Undergraduate probability is desirable, but far more important is calculus and linear algebra (the same background required for a first course covering state space models and control).

A new course based on Part I of the book was introduced at UF in the spring of 2021.  The course is unique in part because no probability theory is required.

Elements of Part II are included in my stochastic control course, and in stochastic methods 2.