Presentation	2004/3/9 Multi-Agent Reinforcement Learning with the Partly High-dimensional State Space Kazuyuki FUJITA, Hiroshi MATSUO,
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Abstract(in English)	In Multi-Agent Reinforcement Learning, each agent observe a state of other agents as a part of environment. Therefore, the state space is exponential in the number of agents and learning speed significantly decrease. Modular Q-learning [6] needs very small state space. However, the incomplete observation involves a decline in the performance. In this paper, we improve Modular Q-learning's performance with the partly high-dimensional state space.
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Keyword(in English)	Multi Agent / Reinforcement Learning / Modular Q-learning / State Space
Paper #	AI2003-92
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Conference Information
Committee	AI
Conference Date	2004/3/9(1days)
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Paper Information
Registration To	Artificial Intelligence and Knowledge-Based Processing (AI)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	Multi-Agent Reinforcement Learning with the Partly High-dimensional State Space
Sub Title (in English)
Keyword(1)	Multi Agent
Keyword(2)	Reinforcement Learning
Keyword(3)	Modular Q-learning
Keyword(4)	State Space
1st Author's Name	Kazuyuki FUJITA
1st Author's Affiliation	Department of Computer Engineering, Nagoya Institute of Technology()
2nd Author's Name	Hiroshi MATSUO
2nd Author's Affiliation	Department of Computer Engineering, Nagoya Institute of Technology
Date	2004/3/9
Paper #	AI2003-92
Volume (vol)	vol.103
Number (no)	725
Page	pp.pp.-
#Pages	6
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