Presentation 2002/3/11
Behavior acquisition for a real robot with a visual sensor by Direct-Vision-Based Reinforcent Learning
Masaru IIDA, Masanori SUGISAKA, Katsunari SHIBATA,
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Abstract(in English) In this paper it is verified that Direct-Vision-Based Reinforcement Learning(RL) is effective in on-line action learning of a real mobile robot with a linear monochrome visual sensor. In Direct-Vision-Based RL, row visual sensory signals are put into a layered neural network directly, and the neural network is trained by Back Propagation using the training signal generated based on reinfbrcement learning. It was confirmed that the robot could obtain appropriate reaching actions to a target object through the learning from scratch without any advance knowledge and any helps of humans . After this learning, the robot could obtain a global representation in the hidden layer by integrating the visual signals, each of which represents local information.
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Keyword(in English) Direct-Vision-Based Reinforcement Learning / real robot / neural network / visual sensor / global representation
Paper # NC2001-154
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Committee NC
Conference Date 2002/3/11(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Behavior acquisition for a real robot with a visual sensor by Direct-Vision-Based Reinforcent Learning
Sub Title (in English)
Keyword(1) Direct-Vision-Based Reinforcement Learning
Keyword(2) real robot
Keyword(3) neural network
Keyword(4) visual sensor
Keyword(5) global representation
1st Author's Name Masaru IIDA
1st Author's Affiliation Department of Electrical and Electronics Engineering, Faculty of Engineering. Oita University()
2nd Author's Name Masanori SUGISAKA
2nd Author's Affiliation Department of Electrical and Electronics Engineering, Faculty of Engineering. Oita University
3rd Author's Name Katsunari SHIBATA
3rd Author's Affiliation Department of Electrical and Electronics Engineering, Faculty of Engineering. Oita University
Date 2002/3/11
Paper # NC2001-154
Volume (vol) vol.101
Number (no) 735
Page pp.pp.-
#Pages 8
Date of Issue