Basic Research on Transfer Learning Indicators for Reinforcement Learning

被引:0
|
作者
Sugikawa, Satoshi [1 ]
Takeoka, Kenta [1 ]
Kotani, Naoki [1 ]
机构
[1] Osaka Inst Technol, 1-79-1 Kitayama, Hirakata, Osaka 5730196, Japan
来源
JOURNAL OF ROBOTICS NETWORKING AND ARTIFICIAL LIFE | 2023年 / 10卷 / 03期
关键词
Reinforcement learning; Transfer learning; Maze problems;
D O I
暂无
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Reinforcement learning requires a lot of time for the agent to learn. Transfer learning methods can be used to shorten this learning time, but they have the disadvantage that it is not known which knowledge is effective in what kind of environment until it is learned. When users transfer knowledge, it is necessary to investigate the relationship between the transfer source and the transfer destination. This study proposes an adaptive criteria evaluation index that can determine this relationship in advance. In the simulation, we confirmed the effectiveness of the proposed method using several problem examples. (c) 2022The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.This is an open access article distributed under the CC BY-NC 4.0 license
引用
收藏
页码:261 / 265
页数:5
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