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arXiv · 2602.11735

AC-MASAC: An Attentive Curriculum Learning Framework for Heterogeneous UAV Swarm Coordination

Wanhao Liu·Junhong Dai·Yixuan Zhang·Shengyun Yin·Panshuo Li
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Abstract

Cooperative path planning for heterogeneous UAV swarms poses significant challenges for Multi-Agent Reinforcement Learning (MARL), particularly in handling asymmetric inter-agent dependencies and addressing the risks of sparse rewards and catastrophic forgetting during training. To address these issues, this paper proposes an attentive curriculum learning framework (AC-MASAC). The framework introduces a role-aware heterogeneous attention mechanism to explicitly model asymmetric dependencies. Moreover, a structured curriculum strategy is designed, integrating hierarchical knowledge transfer and stage-proportional experience replay to address the issues of sparse rewards and catastrophic forgetting. The proposed framework is validated on a custom multi-agent simulation platform, and the results show that our method has significant advantages over other advanced methods in terms of Success Rate, Formation Keeping Rate, and Success-weighted Mission Time. The code is available at \textcolor{red}{https://github.com/Wanhao-Liu/AC-MASAC}.

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Wanhao Liu, Junhong Dai, Yixuan Zhang, Shengyun Yin, Panshuo Li. 2026-02-12. AC-MASAC: An Attentive Curriculum Learning Framework for Heterogeneous UAV Swarm Coordination. https://arxiv.org/abs/2602.11735

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