Markov Decision Processes and Reinforcement Learning

This successor to Martin L. Puterman's widely cited 1994 book on Markov decision processes covers their application reinforcement learning, an area of enormous growth. Providing worked examples, algorithms, and simulations, it is ideal for graduate students, researchers, and professionals in operations research and machine learning.

This book offers a comprehensive introduction to Markov decision process and reinforcement learning fundamentals using common mathematical notation and language. Its goal is to provide a solid foundation that enables readers to engage meaningfully with these rapidly evolving fields. Topics covered include finite and infinite horizon models, partially observable models, value function approximation, simulation-based methods, Monte Carlo methods, and Q-learning. Rigorous mathematical concepts and algorithmic developments are supported by numerous worked examples. As an up-to-date successor to Martin L. Puterman's influential 1994 textbook, this volume assumes familiarity with probability, mathematical notation, and proof techniques. It is ideally suited for students, researchers, and professionals in operations research, computer science, engineering, and economics.