Multi-agent reinforcement learning for planning and scheduling multiple goals

Recently, reinforcement learning has been proposed as an effective method for knowledge acquisition of the multiagent systems. However, most researches on multiagent system applying a reinforcement learning algorithm focus on the method to reduce complexity due to the existence of multiple agents and goals. Though these pre-defined structures succeeded in putting down 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 which is robust in the non-MDPs