Review
“Its remarkable clarity, range, and depth make this a magnificent book both to learn from and to teach. It opens the door to so many modern techniques while firmly grounding them in the statistical and mathematical theory given us by the founders. t is a wonderful book—truly exceptional.”
—Thomas J. Sargent, Department of Economics, New York University, Senior Fellow, Hoover Institution, Stanford University
“I love the topics covered—a great mix of classical approaches and more recent trends. It'll be my main textbook for teaching reinforcement learning.”
—Michael L. Littman, Professor of Computer Science, Brown University
—Thomas J. Sargent, Department of Economics, New York University, Senior Fellow, Hoover Institution, Stanford University
“I love the topics covered—a great mix of classical approaches and more recent trends. It'll be my main textbook for teaching reinforcement learning.”
—Michael L. Littman, Professor of Computer Science, Brown University
About the Author
Mykel Kochenderfer is Associate Professor at Stanford University, where he is Director of the Stanford Intelligent Systems Laboratory (SISL). He is the author of Decision Making Under Uncertainty (MIT Press). Tim Wheeler is a software engineer in the Bay Area, working on autonomy, controls, and decision-making systems. Kochenderfer and Wheeler are coauthors of Algorithms for Optimization (MIT Press). Kyle Wray is a researcher who designs and implements the decision-making systems on real-world robots.
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