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Performance of LQ-learning in POMDP Environments
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- Lee Haeyeon
- Dept. of Elec. & Comm. Eng., School of Eng., Tohoku Univ.
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- Kamaya Hiroyuki
- Dept. of Elec. Eng., Hachinohe National College of Technology
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- Abe Kenich
- Dept. of Elec. & Comm. Eng., School of Eng., Tohoku Univ.
Description
In this paper, we propose a new type of LQ-learning to solve POMDP. In the POMDP environment, the agent cannot observe the environment directly. In the LQ-learning, in order to dicriminate partially observed states, the agent attaches label to each observation which perceived as the same ones. Unlike our previous LQ-learning, we make preparations of knowledge about the environment in advance. The knowledge is automatically acquired by Kohenen’s Self-Organizing Map (SOM), which provides the knowledge about state transitions to the agent. Then, LQ-learning agent attaches labels to observations with reference to a map obtained by SOM.
Journal
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- SICE Annual Conference Program and Abstracts
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SICE Annual Conference Program and Abstracts 2002 (0), 174-174, 2002
The Society of Instrument and Control Engineers
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Keywords
Details 詳細情報について
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- CRID
- 1390282680561053568
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- NII Article ID
- 130006960136
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- Text Lang
- en
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed
