R-learning with Multiple State-action Value Tables
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- Ishikawa Koichiro
- School of Knowledge Science, Japan Advanced Institute of Science and Technology
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- Sakurai Akito
- Department of Administration Engineering, Faculty of Science and Technology, Keio University
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- Fujinami Tsutomu
- School of Knowledge Science, Japan Advanced Institute of Science and Technology
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- Kunifuji Susumu
- School of Knowledge Science, Japan Advanced Institute of Science and Technology
Bibliographic Information
- Other Title
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- 複数の状態行動価値表を用いたR学習の高速化
- フクスウ ノ ジョウタイ コウドウ カチヒョウ オ モチイタ R ガクシュウ ノ コウソクカ
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Abstract
We propose a method to improve the performance of R-learning, a reinforcement learning algorithm, by using multiple state-action value tables. Unlike Q- or Sarsa learning, R-learning learns a policy to maximize undiscounted rewards. Multiple state-action value tables cause substantial explorations as needed and make R-learnings to work well. Efficiency of the proposed method is verified through experiments in simulation environment.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 126 (1), 72-82, 2006
The Institute of Electrical Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390001204604267008
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- NII Article ID
- 10016922097
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 7786275
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed
