Design of Experiments for Reinforcement Learning

This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not com...

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書誌詳細
第一著者: Gatti, Christopher
フォーマット: 図書
言語:English
出版事項: Springer 2015
主題:
オンライン・アクセス:https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/57797
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その他の書誌記述
要約:This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.