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Simultaneous Learning to Acquire Competitive Behaviors in Multi-agent System Based on Modular Learning System

  • Yasutake Takahashi
  • Kazuhiro Edazawa
  • Kentarou Noma
  • Minoru Asada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)

Abstract

The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is a case of RoboCup competitions since other agent behaviors may cause sudden changes in state transition probabilities of which constancy is needed for the learning to converge. The keys for simultaneous learning to acquire competitive behaviors in such an environment are

– a modular learning system for adaptation to the policy alternation of others, and

– an introduction of macro actions for simultaneous learning to reduce the search space.

This paper presents a method of modular learning in a multiagent environment, by which the learning agents can simultaneously learn their behaviors and adapt themselves to the situations as consequences of the others’ behaviors.

Keywords

Reinforcement Learning Multiagent System Real Robot State Transition Probability Competitive Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yasutake Takahashi
    • 1
    • 2
  • Kazuhiro Edazawa
    • 1
  • Kentarou Noma
    • 1
  • Minoru Asada
    • 1
    • 2
  1. 1.Dept. of Adaptive Machine SystemsGraduate School of Engineering 
  2. 2.Handai Frontier Research CenterOsaka UniversityOsakaJapan

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