Reinforcement Learning-Based Cooperative Hunting for an Unmanned Surface Vehicle Swarm

In this paper, reinforcement learning is employed to address the cooperative hunting task for a swarm of underactuated unmanned surface vehicles (USVs) in maritime warfare scenarios. By virtue of the multi-agent deep deterministic policy gradient algorithm, the cooperative swarm hunting strategy (CSHS) is developed by defining new reward functions that can guide the USV swarm in a continuous manner, thereby avoiding sparse rewards. Moreover, Unity3D-based virtual environments are built for cost-efficient learning of the proposed CSHS. The results demonstrate that the CSHS is able to not only avoid obstacles but also exhibits strong generalization among different scenarios, within the entire USV swarm hunting process.