書誌事項
- タイトル別名
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- An Extension of the Rational Policy Making algorithm to Continuous State Spaces
- ゴウリテキ セイサク ケイセイ アルゴリズム ノ レンゾクチ ニュウリョク エノ カクチョウ
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抄録
Reinforcement Learning is a kind of machine learning. We know Profit Sharing, the Rational Policy Making algorithm (RPM), the Penalty Avoiding Rational Policy Making algorithm and PS-r* to guarantee the rationality in a typical class of the Partially Observable Markov Decision Processes. However they cannot treat continuous state spaces. In this paper, we present a solution to adapt them in continuous state spaces. We give RPM a mechanism to treat continuous state spaces in the environment that has the same type of a reward. We show the effectiveness of the proposed method in numerical examples.
収録刊行物
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- 人工知能学会論文誌
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人工知能学会論文誌 22 (3), 332-341, 2007
一般社団法人 人工知能学会
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詳細情報
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- CRID
- 1390282680085982208
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- NII論文ID
- 10022007639
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- NII書誌ID
- AA11579226
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- BIBCODE
- 2007TJSAI..22..332M
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- ISSN
- 13468030
- 13460714
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- NDL書誌ID
- 9603992
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可