書誌事項
- タイトル別名
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- Acceleration of Reinforcement Learning by Efficient Policy Evaluation
- キョウカ ガクシュウ ニ オケル ホウサク ヒョウカ ノ コウリツカ ニ ヨル ガクシュウ ノ カソク
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抄録
Typical methods for solving reinforcement learning problems iterate two steps, policy evaluation and policy improvement. This study proposes algorithms for the policy evaluation to improve learning efficiency. The proposed algorithms, based on the Krylov Subspace Method (KSM), are tens to hundreds times more efficient than existing algorithms based on the Stationary Iterative Methods (SIM). Algorithms based on KSM are far more efficient than they have been generally expected. This study clarifies what makes algorithms based on KSM makes more efficient with numerical examples and theoretical discussions.
収録刊行物
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- 計測自動制御学会論文集
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計測自動制御学会論文集 49 (7), 696-702, 2013
公益社団法人 計測自動制御学会
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詳細情報 詳細情報について
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- CRID
- 1390282679479140992
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- NII論文ID
- 10031188141
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- NII書誌ID
- AN00072392
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- ISSN
- 18838189
- 04534654
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- NDL書誌ID
- 024821264
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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
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- 抄録ライセンスフラグ
- 使用不可