Reinforcement learning of cooperative behaviors for multi-robot tracking of multiple moving targets

Traditional reinforcement learning algorithms learn based on discrete/finite states and actions, thus limit the learned behaviors to discrete/finite space. To address this problem, this paper introduces a distributed reinforcement learning controller that integrates reinforcement learning with behavior based control networks. This learning controller can enable the robot to generate appropriate control policy which combines different elementary behaviors. In addition, to address the problems in concurrent learning, a distributed learning control algorithm is proposed to coordinate concurrent learning processes. The distributed reinforcement learning controller and learning control algorithm are applied to multi-robot tracking of multiple moving targets. The efficacy is demonstrated by simulations.