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A study on Reinforcement Learning system for agents to acquire cooperative behavior in gap-widening situations

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3 Author(s)
Kitakoshi, D. ; Dept. of Comput. Sci., Tokyo Nat. Coll. of Technol., Tokyo, Japan ; Miyauchi, R. ; Suzuki, M.

This article proposes an Interactive Hierarchical Reinforcement Learning system (IH-RL). The goal of our study is that the agents using the IH-RL acquire adequate behaviors to cooperative in “gap-widening” situations. Such situations are observed in a variety of real-world environments (e.g., economic gaps between humans or between companies in a community), and are thus important to solve. Computer simulations are carried out to evaluate the basic performance of our system. The results showed that the IH-RL resolves gap-widening situations through agents' cooperative behaviors.

Published in:

Robotic Intelligence In Informationally Structured Space (RiiSS), 2011 IEEE Workshop on

Date of Conference:

11-15 April 2011

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