Task-oriented reinforcement learning for continuing task in dynamic environment

Md Abdus Samad Kamal, Junichi Murata, Kotaro Hirasawa

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper presents task-oriented reinforcement learning, a modified approach of reinforcement-learning to simplify continuing dynamic problems in a more realistic and human-like way of thinking from the viewpoint of the tasks. In this learning method an agent takes as input the 'state of task' instead of 'state of environment' and chooses appropriate action to achieve the goal of the corresponding task. The proposed system learns from the viewpoint of tasks that enables the system to find and follow a precise policy in a continuing-dynamic environment and offers simple implementation for a multiple agents system.

Original languageEnglish
Pages (from-to)7-12
Number of pages6
JournalResearch Reports on Information Science and Electrical Engineering of Kyushu University
Volume9
Issue number1
Publication statusPublished - Mar 1 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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