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.
CITATION STYLE
Mikami, S., Kakazu, Y., & Fogarty, T. C. (1995). Co-operative Reinforcement Learning by payoff filters (Extended abstract). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 912, pp. 319–322). Springer Verlag. https://doi.org/10.1007/3-540-59286-5_77
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