Study of cooperative algorithm and group behavior in multi-robot

In a multi-agent system, the action selection strategy is important for the cooperation and coordination of multi agents. However the overlap of actions selected individually by each robot makes the acquisition of cooperation behaviors less efficient. In addition to that, a complex and dynamic environment makes cooperation even more difficult. So in this paper, we propose a control algorithm which enables each robot to determine the action for the effective cooperation in multi-robot system. We employ a reinforcement learning in order to choose a proper action for each robot in its action subspace. In this paper, robot soccer system is adopted for the multi-robot environment. To play a soccer game, elementary actions such as shooting and passing must be provided. Q-learning, which is one of the popular methods for reinforcement learning, is used to determine what actions to take. Through simulations, the efficiency of own proposed algorithm is verified for the cooperation in multi robot system.