Reinforcement Learning and Optimal Control (Dimitri P. Bertsekas)

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Reinforcement Learning and Optimal Control (Dimitri P. Bertsekas)

In Reinforcement Learning (RL), one of the most active study topics in artificial intelligence, an agent interacts with a complex, uncertain environment while attempting to maximize the overall number of rewards it receives.

The book's goal is to analyze complex multistage decision problems that, while theoretically, can be resolved by dynamic programming and optimal control, are computationally intractable.

We describe problem-solving techniques that result in suboptimal but workable policies by using approximations. Together, these techniques are known as reinforcement learning, however, they are also known by other names such as approximation dynamic programming and neuro-dynamic programming.

The book's mathematical language differs substantially from that of the author's works on dynamic programming and the monograph they co-wrote with John Tsitsiklis on neuro-dynamic programming.

We depend less on evidence-based insights and more on intuitive justifications. However, in an appendix, we present a thorough short treatment of the theory of finite and infinite horizon dynamic programming as well as several fundamental approximation techniques. We need only a basic understanding of calculus, elementary probability, and matrix-vector algebra for this.

Ebook Details

About the Authors
Professor of electrical engineering and computer science in the School of Engineering at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, Dimitri P. Bertsekas is an applied mathematician, electrical engineer, and computer scientist.
Published Date / Year
276 pages
eBook Format
PDF files

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