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Autonomous, Harmonious and Purposive Acquisition of Various Functions of Robots by Reinforcement Learning and the Relation to the Intelligence Formation

Autonomous, Harmonious and Purposive Acquisition of Various Functions of Robots by Reinforcement Learning and the Relation to the Intelligence Formation
强化学习自主、协调、有目的地获取机器人各种功能及其与智能形成的关系
批准号:
15300064
负责人:
SHIBATA Katsunari
金额:
$4.16万
依托单位:
依托单位国家:
日本
项目类别:
Grant-in-Aid for Scientific Research (B)
财政年份:
2003
资助国家:
日本
项目状态:
已结题
起止时间:
2003 至 2006

项目摘要

项目成果

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中文摘要
翻译
本研究旨在表明,通过使用强化学习衍生的训练信号进行学习,在直接输入传感器信号并输出运动命令的神经网络中,根据需要出现各种功能。主要的水果如下。据说神经网络不擅长符号处理。然而,只有通过强化学习,神经网络的输出表示才会变成二值。结果表明,机器人可以在不提供任何图像处理、图像识别或给定任务信息的情况下,利用神经网络学习推盒行为。结果表明,即使在存在各种物体和彩色传单的准真实世界中,真正的机器人也能在一定程度上学会够到物体。研究表明,通过强化学习训练的递归神经网络可以学习一些被认为与空间或时间抽象相关的任务。
英文摘要
This research was aimed to show that by the learning using the training signals that are derived by reinforcement learning, various functions emerge according to the necessity in a neural network to which sensor signals are directly entered and whose outputs are motor commands. The main fruits are as follows.1. It is said that neural networks are not good at symbol processing. However, it was shown that the output representation of a neural network became binary only by reinforcement learning.2. It was shown that a real robot could learn box-pushing behavior using neural network without giving any informatio a about image processing, image recognition, or the given task.3. It was shown that a real robot could learn to reach an object in some degree even in a quasi-real world where various objects and colorful leaflets exist.4. It was shown that a recurrent neural network trained by reinforcement learning could learn some tasks that are thought to be relevant to the spatial or temporal abstraction.
期刊论文(55)
专著(0)
科研奖励(0)
会议论文
DOI: --
发表时间: 2007
期刊: 計測自動制御学会論文集 Vol. 43, No.1
影响因子: --
作者: [柴田克成, 伊藤宏司]
通讯作者: 伊藤宏司
DOI: --
发表时间: 2004
期刊: Proc. of The 9th AROB (Int'l Sympo. on Artificial Life and Robotics) Vol. 2
影响因子: --
作者: [K.Yuki, M.Sugisaka, K.Shibata]
通讯作者: K.Shibata
An Explanation of Emergence of Reward Expectancy Neurons Usine Reinforcement Learning and Neural Net
使用强化学习和神经网络解释奖励期望神经元的出现
DOI: --
发表时间: 2005
期刊: Abstract Book of Fourteenth Annual Computational Neuroscience Meeting
影响因子: --
作者: [Shinya Ishii, Munetaka Shidara, Katsunari Shibata]
通讯作者: Katsunari Shibata
強化学習による探索行動の学習
使用强化学习学习探索行为
DOI: --
发表时间: 2005
期刊: 計測自動制御学会システム・情報部門学術講演会2005講演論文集
影响因子: --
作者: [Shinya Ishii, Munetaka Shidara, Katsunari Shibata, 柴田克成]
通讯作者: 柴田克成
38
    From "Exploration" To "Thinking" - Development of Chaos Dynamics through Reinforcement Learning
    • 批准号:
      15K00360
    • 项目类别:
      Grant-in-Aid for Scientific Research (C)
    • 资助金额:
      $3.0万
    • 财政年份:
      2015
    • 负责人:
      SHIBATA Katsunari
    • 依托单位:
    Exploration of a Breakthrough Technology for Emergence of Symbol Processing by Neuro-based Reinforcement Learning Considering Time Axis
    • 批准号:
      23500245
    • 项目类别:
      Grant-in-Aid for Scientific Research (C)
    • 资助金额:
      $3.33万
    • 财政年份:
      2011
    • 负责人:
      SHIBATA Katsunari
    • 依托单位:
    A challenge towards how far the emergence of higher functions can be explained by reinforcement learning using a neural network
    • 批准号:
      19300070
    • 项目类别:
      Grant-in-Aid for Scientific Research (B)
    • 资助金额:
      $4.99万
    • 财政年份:
      2007
    • 负责人:
      SHIBATA Katsunari
    • 依托单位:
    海外基金