Path planning of a mobile robot as a discrete optimization problem and adjustment of weight parameters in the objective function by reinforcement learning

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Abstract

In a previous paper, we proposed a solution to path planning of a mobile robot. In our approach, we formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal term, a smoothness term and a collision term. This paper presents a theoretical method using reinforcement learning for adjusting weight parameters in the objective functions. However, the conventional Q-learning method cannot be applied to a non-Markov decision process. Thus, we applied Williams's learning algorithm, REINFORCE, to derive an updating rule for the weight parameters. This is a stochastic hill-climbing method to maximize a value function. We verified the updating rule by experiment.

Original languageEnglish
Title of host publicationRoboCup 2000
Subtitle of host publicationRobot Soccer World Cup IV
Pages315-320
Number of pages6
Publication statusPublished - 2001 Dec 1
Event4th Robot World Cup Soccer Games and Conferences, RoboCup 2000 - Melbourne, VIC, Australia
Duration: 2000 Aug 272000 Sep 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2019 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Robot World Cup Soccer Games and Conferences, RoboCup 2000
CountryAustralia
CityMelbourne, VIC
Period00/8/2700/9/3

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Igarashi, H. (2001). Path planning of a mobile robot as a discrete optimization problem and adjustment of weight parameters in the objective function by reinforcement learning. In RoboCup 2000: Robot Soccer World Cup IV (pp. 315-320). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2019 LNAI).