  Multi-agent formation as well as obstacle avoidance is one of the most
actively studied topics in the field of multi-agent systems. Although some
classic controllers like model predictive control (MPC) and fuzzy control
achieve a certain measure of success, most of them require precise global
information which is not accessible in harsh environments. On the other hand,
some reinforcement learning (RL) based approaches adopt the leader-follower
structure to organize different agents' behaviors, which sacrifices the
collaboration between agents thus suffering from bottlenecks in maneuverability
and robustness. In this paper, we propose a distributed formation and obstacle
avoidance method based on multi-agent reinforcement learning (MARL). Agents in
our system only utilize local and relative information to make decisions and
control themselves distributively. Agent in the multi-agent system will
reorganize themselves into a new topology quickly in case that any of them is
disconnected. Our method achieves better performance regarding formation error,
formation convergence rate and on-par success rate of obstacle avoidance
compared with baselines (both classic control methods and another RL-based
method). The feasibility of our method is verified by both simulation and
hardware implementation with Ackermann-steering vehicles.
