Technical Note: Q-Learning

Christopher J.C.H. Watkins,Peter Dayan

Machine Learning（1992）CCF BSCI 3区

Highbury

被引用2902|浏览120

摘要

$\mathcal{Q}$-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states.

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关键词

$$\mathcal{Q}$$ -learning,reinforcement learning,temporal differences,asynchronous dynamic programming