A Reinforcement Learning System Embedded Agent with Neural Network-Based Multi-Valued Pattern Memory Structure

Masanao Obayashi; Tomohiro Nishida; Takashi Kuremoto; Kunikazu Kobayashi; Liang-Bing Feng

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자료유형: 학술대회자료

저자정보: Masanao Obayashi (Yamaguchi University) Tomohiro Nishida (Yamaguchi University) Takashi Kuremoto (Yamaguchi University) Kunikazu Kobayashi (Yamaguchi University) Liang-Bing Feng (Yamaguchi University)

저널정보: 제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2010

발행연도: 2010.10

수록면: 176 - 181 (6page)

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이 논문의 연구 히스토리 (2)

2010

A Reinforcement Learning System Embedded Agent with Neural Network-Based Multi-Valued Pattern Memory Structure

Masanao Obayashi , Tomohi , o Nishida 외 4명 제어로봇시스템학회 국제학술대회 논문집 2010.10 학술대회자료

2008

A Reinforcement Learning System with Chaotic Neural Networks-Based Adaptive Hierarchical Memory Structure for Autonomous Robots

Masanao Obayashi , Kenichi , o Na 외 4명 제어로봇시스템학회 국제학술대회 논문집 2008.10 학술대회자료

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This paper concerns about a way of intellectualization of robots (called "agent" here). Human learns incidents by own actions and reflects them on the subsequent actions as own experiences. These experiences are memorized in his/her brain and recollected and reused if necessary. This research incorporates such an intelligent information processing mechanism, and applies it to an autonomous agent that has three main functions that is, learning, memorization and associative recollection. In the proposed system, an actor-critic type reinforcement learning method is used for learning. For memorization, we introduce the chaotic auto-associative model that is proposed by Chartier, and that is also used like mutual associative memory system. Moreover, to deal with the increase of information, the memory part has an adaptive hierarchical layered structure of the memory module that consists of chaotic neural networks, especially for multi-valued pattern. Finally, the effectiveness of this proposed method is verified through the simulation applied to the maze-searching problem.

#Reinforcement learning #Multi-valued associative memory #Chaotic neural network #Intelligent agent

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