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Optimal Decision-Making in a Known Environment

In: Reinforcement Learning From Scratch

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  • Uwe Lorenz

Abstract

This section describes how to compute an optimal action strategy for an environment with a finite number of states and action possibilities. You will learn the difference between an off-policy and an on-policy evaluation of state transitions. Value iteration and iterative tactic search techniques will be introduced and applied and tried in practice scenarios using the Java Hamster. Iterative tactic search, as a mutual improvement of evaluation and control, is introduced as a generalizable strategy for finding optimal behavior. Furthermore, the basics of computing optimal moves in a manageable board game scenario with adversaries are described.

Suggested Citation

  • Uwe Lorenz, 2022. "Optimal Decision-Making in a Known Environment," Springer Books, in: Reinforcement Learning From Scratch, chapter 3, pages 23-46, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-09030-1_3
    DOI: 10.1007/978-3-031-09030-1_3
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