Reinforcement Learning of Multi-Agents on Real Number Environment

DOI
  • Umano Motohide
    Department of Mathematics and Information Sciences Graduate School of Science, Osaka Prefecture Univesity
  • Shoji Toshihiro
    Department of Mathematics and Information Sciences Graduate School of Science, Osaka Prefecture Univesity
  • Hosoya Yu
    Department of Mathematics and Information Sciences Graduate School of Science, Osaka Prefecture Univesity
  • Seta Kazuhisa
    Department of Mathematics and Information Sciences Graduate School of Science, Osaka Prefecture Univesity

Bibliographic Information

Other Title
  • 実数値環境におけるマルチエージェントの強化学習
  • Consideration on Result of Numerical Simulation
  • 数値実験結果の検討

Abstract

A pursuit game is a multi-agents' benchmark problem, where 4 blue agents pursue and capture a red agent on a grid environment. In the previous research, we extended a grid environment to a real number one and proposed a method of fuzzy Q-learning with the state of fuzzy sets. In this research, we perform a numerical simulation with the environment and algorithm in the previous research. We have a result that we only need a small number of states to solve the problem in the previous research. We have reasons that the red agent with random movement does not get away from blue agents in spite that the red one has the same speed as blue ones, a blue agent has a large capture range and only 3 blue agents can often capture the red one.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390001205666944768
  • NII Article ID
    130004730231
  • DOI
    10.14864/fss.23.0.295.0
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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