In a multi robots environment, the overlap of actions selected by each robot makes the acquisition of cooperation behaviors less efficient. In this paper an approach is proposed to determine the action selection priority level based on which the cooperative behaviors can be readily controlled. First, eight levels are defined for the action selection priority, which can be correspondingly mapped to eight subspaces of actions. Second, using the local potential field method, the action selection priority level for each robot is calculated and thus its action subspace is obtained. Then, Reinforcement learning (RL) is utilized to choose a proper action for each robot in its action subspace. Finally, the proposed method has been implemented in a soccer game and the high efficiency of the proposed scheme was verified by the result of both the computer simulation and the real experiments.%在多机器人环境中,由于每个机器人动作选择的重叠现象,让机器人之间的协作变得很差.提出了一个方法用于确定动作选择级别.在此基础上,可以很好地控制多机器人的协作行为的获取.首先,定义了用于动作选择级优先级的8个级别,这8个级别相应的映射到8个动作子空间.然后,利用局部势场法,每个机器人的动作选择优先级被计算出来,并且因此,每个机器人获得了各自需要搜索的动作子空间.在动作子空间中,每个机器人利用加强学习方法来选择一个适当的动作.最终,把该方法用于机器人足球比赛的机器人局部协作训练中.试验的效果在仿真和实际比赛中得到了证实.
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