XAI-Driven Multi-Agent Reinforcement Learning for Swarm USV Continuous Multi-Target Hunting

Abstract

Swarm unmanned surface vehicle (USV) has become a promising solution for maritime security and defense activities. However, the complexity of continuous multi-target hunting presents significant challenges for multi-agent coordination in water environments. This study proposes an XAI-driven multi-agent reinforcement learning (MARL) framework for swarm USVs to perform continuous multi-target hunting. The framework integrates multi-agent deep reinforcement learning (basic MAPPO and MAPPO-LSTM) for cooperative hunting policies with explainable AI (XAI) mechanisms to provide interpretable insights into swarm behaviors and decision strategies. Experimental results in a 3D simulation platform environment demonstrate that MAPPO-LSTM achieves superior interception performance, reducing mean time-to-capture and improving trajectory smoothness compared to the baseline MAPPO. Furthermore, the proposed XAI pipeline combines distance-based importance attribution, influence graph analysis, strategic clustering, and temporal dynamics evaluation to explain agent contributions, inter-agent dependencies, emergent strategies, and efficiency patterns. By providing multi-level interpretability, the framework enhances transparency, trust, and deployment readiness of swarm USVs in dynamic maritime defense scenarios.