多智能体强化学习让水下无人机协作探测更隐蔽高效
Cooperative Target Detection with AUVs: A Dual-Timescale Hierarchical MARDL Approach
- 分层双时尺度策略，高层决策参与任务者，低层控制功率与轨迹
- 仿真显示收敛快，性能优于基准算法，兼顾隐蔽性与长期效率
- 适合水下侦察、反潜等需隐蔽协同的军事或科研任务
自主水下航行器（AUV）在协同探测与侦察方面展现出巨大潜力。然而，协同通信会带来暴露风险。在对抗环境中，如何在保证隐蔽性的同时实现高效协作，成为水下协同任务的关键挑战。本文提出一种新型双时尺度分层多智能体近端策略优化（H-MAPPO）框架：高层由中心AUV决定任务参与者，低层通过参与AUV的功率与轨迹控制降低暴露概率。仿真结果表明，该框架具备快速收敛能力，在性能上优于基准算法，能在确保隐蔽操作的前提下最大化长期协作效率。
原文摘要 · Abstract (English)
Autonomous Underwater Vehicles (AUVs) have shown great potential for cooperative detection and reconnaissance. However, collaborative AUV communications introduce risks of exposure. In adversarial environments, achieving efficient collaboration while ensuring covert operations becomes a key challenge for underwater cooperative missions. In this paper, we propose a novel dual time-scale Hierarchical Multi-Agent Proximal Policy Optimization (H-MAPPO) framework. The high-level component determines the individuals participating in the task based on a central AUV, while the low-level component reduces exposure probabilities through power and trajectory control by the participating AUVs. Simulation results show that the proposed framework achieves rapid convergence, outperforms benchmark algorithms in terms of performance, and maximizes long-term cooperative efficiency while ensuring covert operations.
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