Multi-robot concurrent learning of cooperative behaviours for the tracking of multiple moving targets

Reinforcement learning has been extensively studied and applied for generating cooperative behaviours in multi-robot systems. However, traditional reinforcement learning algorithms assume discrete state and action spaces with finite number of elements. This limits the learning to discrete behaviours and cannot be applied to most real multi-robot systems that inherently require appropriate combinations of different elementary behaviours. To address this problem, we design a distributed learning controller that integrates reinforcement learning with behaviour-based control networks. This learning controller can enable the robots to generate appropriate control policy without the need for human design or hardcoding. Furthermore, to address the problems in concurrent learning, we propose a distributed learning control algorithm to coordinate the concurrent learning processes. The distributed learning controller and learning control algorithm are applied to multi-robot tracking of multiple moving targets. The efficacy of our proposed scheme is shown through simulations.