|Algorithms for Decision Making (MIT Press)|
|Friday, 19 August 2022|
This book provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. Mykel Kochenderfer and Tim Wheeler first address the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turn to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain.
The book goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in Julia.
Author: Mykel Kochenderfer and Tim Wheeler
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