AC-MASAC：一种用于异构无人机群协调的注意力课程学习框架
AC-MASAC: An Attentive Curriculum Learning Framework for Heterogeneous UAV Swarm Coordination
AI总结：
本文提出AC-MASAC框架，通过角色感知注意力机制和结构化课程策略，解决异构无人机群协同路径规划中的稀疏奖励和灾难性遗忘问题，提升成功率和任务完成效率。
摘要：
异构无人机群的协同路径规划对多智能体强化学习（MARL）构成了重大挑战，特别是在处理智能体间不对称依赖关系以及解决训练过程中稀疏奖励和灾难性遗忘的风险方面。为了解决这些问题，本文提出了一种注意力课程学习框架（AC-MASAC）。该框架引入了角色感知的异构注意力机制，以显式建模不对称依赖关系。此外，设计了一种结构化的课程策略，整合了分层知识传递和阶段比例经验回放，以解决稀疏奖励和灾难性遗忘的问题。所提出的框架在定制的多智能体仿真平台上进行了验证，结果表明，我们的方法在成功率、保持率和加权任务时间方面均优于其他先进方法。代码可在https://github.com/Wanhao-Liu/AC-MASAC上获得。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
