Kei Matsubayashi

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Reinforcement learning is a technique to learn suitable action policies that maximize utility, via the clue of reinforcement signals: reward or punishment. Q-learning, a widely used reinforcement learning method, has been analyzed in much research on autonomous agents. However, as the size of the problem space increases , agents need more computational(More)
In multi-agent environments where agents independently generate and execute plans to satisfy their goals, the resulting plans may sometimes overlap. In this paper, we propose a collaboration mechanism using social law, through which rational agents can smoothly delegate and receive the execution of the overlapping parts of plans in order to reduce the cost(More)
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