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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/12961
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| タイトル: | State Evaluation Strategy for Exemplar-Based Policy Optimization of Dynamic Decision Problems |
| 著者: | Ikeda, Kokolo
Kita, Hajime |
| 発行日: | 2007 |
| 出版者: | Institute of Electrical and Electronics Engineers (IEEE) |
| 誌名: | 2007 IEEE Congress on Evolutionary Computation (CEC 2007) |
| 開始ページ: | 3685 |
| 終了ページ: | 3691 |
| DOI: | 10.1109/CEC.2007.4424950 |
| 抄録: | Direct policy search (DPS) that optimizes the parameters of a decision making model, combined with evolutionary algorithms which enable robust optimization, is a promising approach to dynamic decision problems. Exemplar- based policy (EBP) optimization is a novel framework for DPS in which the policy is composed of a set of exemplars and a case-based action selector, with the set of exemplars being refined and evolved using a GA. In this paper, state evaluation type EBP representations are proposed for the problem class whose state transition can be predicted. For example, the vector-real representation defines pairs of feature vector and its desirability as exemplars, and evaluate the predicted next states using the exemplars. The state evaluation type EBP-based optimization procedures are shown to be superior to conventional state-action type EBP optimization through application to the Tetris game. |
| Rights: | This is the author's version of the work. Copyright (C) 2007 IEEE. 2007 IEEE Congress on Evolutionary Computation (CEC 2007), 2007, 3685-3691. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| URI: | http://hdl.handle.net/10119/12961 |
| 資料タイプ: | author |
| 出現コレクション: | b11-1. 会議発表論文・発表資料 (Conference Papers)
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このアイテムのファイル:
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記述 |
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| cec2007.pdf | | 822Kb | Adobe PDF | 見る/開く |
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