Extended Abstracts

Machine Learning: ECML-95

Volume 912 of the series Lecture Notes in Computer Science pp 319-322


Co-operative Reinforcement Learning by payoff filters (Extended abstract)

  • Sadayoshi MikamiAffiliated withHokkaido UniversityUniversity of the West of England
  • , Yukinori KakazuAffiliated withHokkaido University
  • , Terence C. FogartyAffiliated withUniversity of the West of England

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This paper proposes an extension of Reinforcement Learning (RL) to acquire co-operation among agents. The idea is to learn filtered payoff that reflects a global objective function but does not require mass communication among agents. It is shown that the acquisition of two typical co-operation tasks is realised by preparing simple filter functions: an averaging filter for co-operative tasks and an enhancement filter for deadlock prevention tasks. The performance of these systems was tested through computer simulations of n-persons prisoner's dilemma, and a traffic control problem.