マルチエージェントシステムにおける多目的強化学習による競合回避行動の獲得

抄録

Multi-agent system (MAS) is constructed by many autonomous agents. Conflicts occur in MAS because of complex interactions among many agents. An agent needs to carry out a task and to avoid conflicts at same time. That is, each agent has to achieve the contradicting purposes. Therefore, this paper proposes a new approach by using multi-objective reinforcement learning as decision making system of an agents. We investigate the efficiency of the proposed approach through a simulation experiment that two agents pass each other in the narrow path.