Applying a Neural Network Architecture with Spatio-Temporal Connections to the Maze Exploration

被引：0

作者：

Filin, Dmitry ^{[1
]}

Panov, Aleksandr I. ^{[1
,2
]}

机构：

[1] Natl Res Univ, Higher Sch Econ, Moscow, Russia

[2] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow, Russia

来源：

BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES (BICA) FOR YOUNG SCIENTISTS | 2018年 / 636卷

关键词：

D O I：

10.1007/978-3-319-63940-6_8

中图分类号：

B84 [心理学]; C [社会科学总论]; Q98 [人类学];

学科分类号：

03 ; 0303 ; 030303 ; 04 ; 0402 ;

摘要：

We present a model of Reinforcement Learning, which consists of modified neural-network architecture with spatio-temporal connections, known as Temporal Hebbian Self-Organizing Map (THSOM). A number of experiments were conducted to test the model on the maze solving problem. The algorithm demonstrates sustainable learning, building a near to optimal routes. This work describes an agents behavior in the mazes of different complexity and also influence of models parameters at the length of formed paths.

引用

页码：57 / 64

页数：8

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