文章基本信息

标题：AN EXPERIMENTAL STUDY FOR TRACKING ABILITY OF DEEP Q-NETWORK
本地全文：下载
作者：Masashi SUGIMOTO ; Ryunosuke UCHIDA ; Kentarou KURASHIGE 等
期刊名称：International Journal of New Computer Architectures and their Applications
印刷版ISSN：2220-9085
出版年度：2020
卷号：10
期号：3
页码：32-38
DOI：10.17781/P002679
语种：English
出版社：Society of Digital Information and Wireless Communications
摘要：Reinforcement Learning (RL) had been attracting attention for a long time that because it can be easily applied to real robots. On the other hand, in Q-Learning, since the Q-table is updated, a large amount of Q-tables are required to express continuous“ states,” such as smooth movements of the robot arm. There was a disadvantage that calculation could not be performed real-time. Deep Q-Network (DQN), on the other hand, uses convolutional neural network to estimate the Q-value itself, so that it can obtain an approximate function of the Q-value. From this characteristics of calculation, this method has been attracting attention, in recent. On the other hand, it seems to the following of multitasking and moving goal point that Q-Learning was not good at has been inherited by DQN. In this paper, to confirm the weak points of DQN by changing the exploration ratio as known as epsilon dynamically, has been tried.