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Value function representation method of reinforcement learning and apparatus using this

机译:强化学习的价值函数表示方法及装置

摘要

Reinforcement learning is one of the intellectual operations applied to autonomously moving robots etc. It is a system having excellent sides, for example, enabling operation in unknown environments. However, it has the basic problem called the “incomplete perception problem”. A variety of solution has been proposed, but none has been decisive. The systems also become complex. A simple and effective method of solution has been desired.;A complex value function defining a state-action value by a complex number is introduced. Time series information is introduced into a phase part of the complex number value. Due to this, the time series information is introduced into the value function without using a complex algorithm, so the incomplete perception problem is effectively solved by simple loading of the method.
机译:强化学习是应用于自主移动机器人等的智力操作之一。它是一种具有出色功能的系统,例如,可以在未知环境中进行操作。但是,它具有一个基本问题,称为“不完全感知问题”。已经提出了多种解决方案,但是没有一个是决定性的。系统也变得复杂。期望有一种简单有效的解决方法。引入了用复数定义状态作用值的复数函数。时间序列信息被引入复数值的相位部分。因此,在不使用复杂算法的情况下将时间序列信息引入到值函数中,因此通过简单加载该方法可以有效地解决不完全感知问题。

著录项

  • 公开/公告号US8175982B2

    专利类型

  • 公开/公告日2012-05-08

    原文格式PDF

  • 申请/专利权人 TOMOKI HAMAGAMI;TAKESI SHIBUYA;

    申请/专利号US20060065558

  • 发明设计人 TOMOKI HAMAGAMI;TAKESI SHIBUYA;

    申请日2006-08-18

  • 分类号G06F15/18;G06E1;

  • 国家 US

  • 入库时间 2022-08-21 17:26:18

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