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Technical Note: Q-Learning

Technical Note: Q-Learning $$\mathcal{Q}$$ -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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Learning Springer Journals

Technical Note: Q-Learning

Machine Learning , Volume 8 (4) – Aug 26, 2004

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References (8)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Control, Robotics, Mechatronics; Artificial Intelligence; Simulation and Modeling; Natural Language Processing (NLP)
ISSN
0885-6125
eISSN
1573-0565
DOI
10.1023/A:1022676722315
Publisher site
See Article on Publisher Site

Abstract

$$\mathcal{Q}$$ -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.

Journal

Machine LearningSpringer Journals

Published: Aug 26, 2004

There are no references for this article.