  Multi-Robot System (MRS) has garnered widespread research interest and
fostered tremendous interesting applications, especially in cooperative control
fields. Yet little light has been shed on the compound ability of formation,
monitoring and defence in decentralized large-scale MRS for pursuit avoidance,
which puts stringent requirements on the capability of coordination and
adaptability. In this paper, we put forward a decentralized Imitation learning
based Alternative Multi-Agent Proximal Policy Optimization (IA-MAPPO) algorithm
to provide a flexible and communication-economic solution to execute the
pursuit avoidance task in well-formed swarm. In particular, a
policy-distillation based MAPPO executor is firstly devised to capably
accomplish and swiftly switch between multiple formations in a centralized
manner. Furthermore, we utilize imitation learning to decentralize the
formation controller, so as to reduce the communication overheads and enhance
the scalability. Afterwards, alternative training is leveraged to compensate
the performance loss incurred by decentralization. The simulation results
validate the effectiveness of IA-MAPPO and extensive ablation experiments
further show the performance comparable to a centralized solution with
significant decrease in communication overheads.
