异构AAV物流任务分配：一种强化学习增强的重叠联盟形成博弈方法
Heterogeneous AAV Logistics Task Allocation: A Reinforcement Learning Enhanced Overlapping Coalition Formation Game Approach
AI总结：
针对动态城市物流中时间敏感任务的随机出现带来的异构AAV任务分配最优性挑战，提出一种基于Transformer的软演员-评论家网络增强的重叠联盟形成博弈方法，实现全局最优任务分配并证明收敛至纳什稳定均衡。
摘要：
在动态城市物流中，时间敏感任务的随机出现对异构AAV物流任务分配提出了显著的最优性挑战。为解决这一问题，提出了一种强化学习增强的重叠联盟形成博弈方法。建立了动态任务分配模型，其中全局最优性通过耦合服务质量与资源消耗的广义物流成本进行数学量化。为应对随机订单到达引起的时变任务集，设计了一种基于Transformer的软演员-评论家网络。通过利用多头自注意力编码可变长度的物流状态并捕获任务间的时空依赖关系，学习到的策略自适应地指导联盟更新，取代重叠联盟形成博弈中的启发式规则。在此基础上，异构AAV可以为动态物流任务形成更高效的重叠联盟。所得到的联盟形成过程被证明构成一个精确势博弈，保证了在有限迭代次数内收敛到纳什稳定均衡。数值仿真表明，所提算法在广义物流成本准则下有效提高了任务分配的最优性。在32架AAV和80个任务的场景中，与启发式OCF基线相比，我们的算法实现了39.76%的成本降低。室内飞行实验进一步验证了其实用性。
英文摘要：
In dynamic urban logistics, the stochastic emergence of time-sensitive tasks poses a significant optimality challenge for heterogeneous AAVs logistics task allocation. To address this problem, a reinforcement learning enhanced overlapping coalition formation game approach is proposed. A dynamic task allocation model is established, where global optimality is mathematically quantified by a generalized logistics cost coupling service quality and resource consumption. To deal with the time-varying task sets induced by stochastic order arrivals, a transformer-based soft actor-critic network is designed. By leveraging multi-head self-attention to encode variable-length logistics states and capture task-wise spatiotemporal dependencies, the learned policy adaptively guides coalition updates, replacing heuristic rules in the overlapping coalition formation game. On this basis, heterogeneous AAVs can form more efficient overlapping coalitions for dynamic logistics tasks. The resulting coalition formation process is proven to constitute an exact potential game, which guarantees convergence to a Nash-stable equilibrium within a finite number of iterations. Numerical simulations demonstrate that the proposed algorithm effectively improves the optimality of task allocation under the generalized logistics cost criterion. In a scenario with 32 AAVs and 80 tasks, our algorithm achieves a 39.76% cost reduction compared with the heuristic OCF baseline. Indoor flight experiments further validate its practicality.
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。
