• Title: Reinforcement Learning and Optimal Control
• Author(s) Dimitri P. Bertsekas
• Publisher: Athena Scientific 2019
• Hardcover/Paperback: 276 pages
• eBook: PDF files
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
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Book Description

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.

• Dimitri P. Bertsekas is an applied mathematician, electrical engineer, and computer scientist, and a professor at the department of Electrical Engineering and Computer Science in School of Engineering at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.

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• Machine Learning
• Linear Programming, Optimization, Approximation, etc.
• Neural Networks and Depp Learning
• Artificial Intelligence
• Statistics, Mathematical Statistics, and SAS Programming
• Probability and Stochastic Processe

• Reinforcement Learning and Optimal Control (Dimitri P. Bertsekas)
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