  Multi-agent reinforcement learning based methods are significant for online
planning of feasible and safe paths for agents in dynamic and uncertain
scenarios. Although some methods like fully centralized and fully decentralized
methods achieve a certain measure of success, they also encounter problems such
as dimension explosion and poor convergence, respectively. In this paper, we
propose a novel centralized training with decentralized execution method based
on multi-agent reinforcement learning to solve the dynamic obstacle avoidance
problem online. In this approach, each agent communicates only with the central
planner or only with its neighbors, respectively, to plan feasible and safe
paths online. We improve our methods based on the idea of model predictive
control to increase the training efficiency and sample utilization of agents.
The experimental results in both simulation, indoor, and outdoor environments
validate the effectiveness of our method. The video is available at
https://www.bilibili.com/video/BV1gw41197hV/?vd_source=9de61aecdd9fb684e546d032ef7fe7bf
