Proposal of PSwithEFP and its Evaluation in Multi-Agent Reinforcement Learning | Zendy

Kazuteru Miyazaki | Zendy; Koudai Furukawa | Zendy; Hiroaki Kobayashi | Zendy

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Proposal of PSwithEFP and its Evaluation in Multi-Agent Reinforcement Learning

Author(s) -

Kazuteru Miyazaki,

Koudai Furukawa,

Hiroaki Kobayashi

Publication year - 2017

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2017.p0930

Subject(s) - computer science , reinforcement learning , adaptability , task (project management) , action selection , artificial intelligence , machine learning , selection (genetic algorithm) , action (physics) , error driven learning , ecology , perception , physics , management , quantum mechanics , neuroscience , economics , biology

When multiple agents learn a task simultaneously in an environment, the learning results often become unstable. This problem is known as the concurrent learning problem and to date, several methods have been proposed to resolve it. In this paper, we propose a new method that incorporates expected failure probability (EFP) into the action selection strategy to give agents a kind of mutual adaptability. The effectiveness of the proposed method is confirmed using Keepaway task.

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