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

• 池田, 心

• 小林, 重信

• 喜多, 一

書誌事項

タイトル別名

• Exemplar-Based Policy with Selectable Strategies and its Optimization Using GA

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.

identifier:https://dspace.jaist.ac.jp/dspace/handle/10119/10914

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 25 (2), 351-362, 2010-02-01

  人工知能学会

キーワード

• direct policy search
• genetic algorithm
• case based reasoning
• Markov decision process
• exemplar

詳細情報

• CRID: 1050845762468512000
• NII論文ID: 120005173797
• ISSN: 13460714
• Web Site: http://hdl.handle.net/10119/10914
• 本文言語コード: ja
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles
  • KAKEN