書誌事項
- タイトル別名
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- Reinforcement Learning Using Adaptive Search Method
- テキオウテキ タンサクホウ オ モチイタ キョウカ ガクシュウ
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抄録
We propose an adaptive probability density function (PDF) to select an effective action on reinforcement learning (RL). The uniform distribution function and the normal distribution function of an action are often used to select an action. When these fuctions are used, however, the information of search direction is net considered. The proposed method utilizing the information of it enables RL to reduce the number of trials, which is needed to real environment learning. Furthermore, the proposed method can be applied easily to various methods of RL, for example, actor-critic, stochastic gradient ascent method. The performance of our proposed method is demonstrated by computer simulations.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 122 (3), 374-380, 2002
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390001204610778240
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- NII論文ID
- 130006845666
- 10007790520
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
- http://id.crossref.org/issn/03854221
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- NDL書誌ID
- 6089026
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可