Presentation	1998/3/19 Modular Reinforcement Learning Satoshi Yamada,
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Abstract(in English)	We propose a modular reinforcement learning system which contains control modules and a selection module. The selection module selects an appropriate control module dependent on states. Both the control modules and the selection module are trained by Q-learning. The modular reinforcement learning was applied to the navigation and target-collection control of Khepera robot. Inputs of each control module are part of whole inputs, and inputs of the selection module are maximum or minimum of Q-values calculated in the control modules. The modular reinforcement learning system learned the navigation and target-collection control faster than reinforcement learning with a single network because it reduced the searching space for reinforcement learning.
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Keyword(in English)	modular reinforcement learning / Khepera robot / selection module / control module
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Conference Information
Committee	NC
Conference Date	1998/3/19(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	Modular Reinforcement Learning
Sub Title (in English)
Keyword(1)	modular reinforcement learning
Keyword(2)	Khepera robot
Keyword(3)	selection module
Keyword(4)	control module
1st Author's Name	Satoshi Yamada
1st Author's Affiliation	Advanced Technology R&D Center, Mitsubishi Electric Corporation()
Date	1998/3/19
Paper #
Volume (vol)	vol.97
Number (no)	623
Page	pp.pp.-
#Pages	8
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