Cooperative Action Acquisition Based on Intention Estimation in a Multi-Agent Reinforcement Learning System

被引:1
|
作者
Tsubakimoto, Tatsuya [1 ]
Kobayashi, Kunikazu [1 ]
机构
[1] Aichi Prefectural Univ, Sch Informat Sci & Technol, Nagakute, Aichi, Japan
关键词
multi-agent; reinforcement learning; Q-learning; cooperation; intention estimation;
D O I
10.1002/ecj.11821
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we propose a method to acquire a series of cooperative actions to reach an appropriate goal without the designer controlling the reward. To accomplish this, we introduce a new concept of "reward interpretation." This is the idea that an agent can increase or decrease the reward given by the environment through the reward interpretation on its won. We applied this idea to the Q-learning method. The simulation results show that the proposed method is superior to a standard Q-learning method and a Q-learning method with cooperation in terms of the number of successful instances of cooperation. (C) 2017 Wiley Periodicals, Inc.
引用
收藏
页码:3 / 10
页数:8
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