|
JAIST Repository >
b. 情報科学研究科・情報科学系 >
b10. 学術雑誌論文等 >
b10-1. 雑誌掲載論文 >
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/10119/10914
|
| タイトル: | 多様な戦略選択を可能にする事例ベースの政策表現とそのGAによる最適化 |
| 著者: | 池田, 心
小林, 重信
喜多, 一 |
| キーワード: | direct policy search
genetic algorithm
case based reasoning
Markov decision process
exemplar |
| 発行日: | 2010/02/01 |
| 出版者: | 人工知能学会 |
| 誌名: | 人工知能学会論文誌 |
| 巻: | 25 |
| 号: | 2 |
| 開始ページ: | 351 |
| 終了ページ: | 362 |
| DOI: | 10.1527/tjsai.25.351 |
| 抄録: | As an approach for dynamic control problems and decision making problems, usually formulated as Markov Decision Processes (MDPs), we focus direct policy search (DPS), where a policy is represented by a model with parameters, and the parameters are optimized so as to maximize the evaluation function by applying the parameterized policy to the problem. In this paper, a novel framework for DPS, an exemplar-based policy optimization using genetic algorithm (EBP-GA) is presented and analyzed. In this approach, the policy is composed of a set of virtual exemplars and a case-based action selector, and the set of exemplars are selected and evolved by a genetic algorithm. Here, an exemplar is a real or virtual, free-styled and suggestive information such as ``take the action A at the state S'' or ``the state S1 is better to attain than S2''. One advantage of EBP-GA is the generalization and localization ability for policy expression, based on case-based reasoning methods. Another advantage is that both the introduction of prior knowledge and the extraction of knowledge after optimization are relatively straightforward. These advantages are confirmed through the proposal of two new policy expressions, experiments on two different problems and their analysis. |
| Rights: | Copyright (C) 2010 人工知能学会. 池田心, 小林重信, 喜多一, 人工知能学会論文誌, 25(2), 2010, 351-362. http://dx.doi.org/10.1527/tjsai.25.351 |
| URI: | http://hdl.handle.net/10119/10914 |
| 資料タイプ: | publisher |
| 出現コレクション: | b10-1. 雑誌掲載論文 (Journal Articles)
|
このアイテムのファイル:
| ファイル |
記述 |
サイズ | 形式 |
|---|
| 16019.pdf | | 673Kb | Adobe PDF | 見る/開く |
|
当システムに保管されているアイテムはすべて著作権により保護されています。
|
