Multi-agent reinforcement learning for planning and scheduling multiple goals

Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition of multiagent systems. However, most research on multiagent systems applying a reinforcement learning algorithm, focus on a method to reduce complexity due to the existence of multiple agents and goals. Although these pre-defined structures succeeded in lessening the undesirable effect due to the existence of multiple agents, they would also suppress the desirable emergence of cooperative behaviors in the multiagent domain. We show that the potential cooperative properties among the agent are emerged by means of profit-sharing (J. Grefenstette, 1988; K. Miyazaki et al., 1994) which is robust in the non-MDPs.