Action learning to single robot using MARL with repeated consultation: Realization of repeated consultation interruption for the adaptation to environmental change

Abstract

We have proposed multi-agent reinforcement learning with repeated consultation (MARLRC) as a multi-agent reinforcement learning that agents can select the concerted action. In MARLRC, agents select a virtual action and share it with other agents several times in one robot decision-making. In this study, we focused on the problem that MARLRC does not take into account the environment that time constraints may occur in the decision-making. As an approach to solve this problem, we considered to introduce to determine the amount of time that can be used in decision-making and decision-making in predetermined time. We introduced the method to decision-making in time predetermined by MARLRC in this study.