Work

Multi-Agent Reinforcement Learning-Based Cooperative Encirclement Control of Autonomous Surface Vehicles Against Multiple Targets

Year: 2025

Type: article

Abstract: Autonomous surface vehicles (ASVs) have been widely applied in ocean engineering due to their small size, low cost, and high mobility. However, more relevant encirclement control methods with many-to-one are simple and do not consider the system dynamics. This article proposes a cooperative encirclement control method for ASVs against multiple targets based on multi-agent reinforcement learning. Firstly, a dynamic target allocation algorithm is designed based on location information of both vehicles and targets, enabling vehicles to select encirclement targets in real-time according to relative distances. Subsequently, the whole encirclement process is divided into multiple stages, and a multi-stage reward function is developed based on curriculum learning to guide ASVs in completing encirclement tasks progressively, from simpler to more complex scenarios. Then, the actor and critic networks incorporating long short-term memory are constructed, respectively, and a multi-agent soft actor-critic reinforcement learning algorithm is employed to train ASVs, enhancing cooperative target encirclement maneuvers. Finally, the effectiveness and superiority of the proposed method is validated through a six-on-two encirclement simulation.

Source: Journal of Marine Science and Engineering

DOI: 10.3390/jmse13081558

Authors: Xingru Qu, Chu Li, Shang Jiang, Guanqun Liu, Rubo Zhang

Institutions: Dalian Minzu University, Dalian Naval Academy

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities