A Period Training Method for Heterogeneous UUV Dynamic Task Allocation

Abstract

In the dynamic task allocation of unmanned underwater vehicles (UUVs), the schemes of UUVs need to be quickly reallocated to respond to emergencies. The most common heuristic allocation method uses predesigned optimization rules to iteratively obtain a solution, which is time-consuming. To quickly assign tasks to heterogeneous UUVs, we propose a novel task allocation algorithm based on multi-agent reinforcement learning (MARL) and a period training method (PTM). The period training method (PTM) is used to optimize the parameters of MARL models in different training environments, improving the algorithm’s robustness. The simulation results show that the proposed methods can effectively allocate tasks to different UUVs within a few seconds and reallocate the schemes in real time to deal with emergencies.