具有避撞和切换感知小区关联的多无人机速度控制：采用动作分支的深度强化学习
Multi-UAV Speed Control with Collision Avoidance and Handover-aware Cell Association: DRL with Action Branching
AI总结：
针对3D空中高速公路多无人机速度与小区关联优化问题，提出带动作分支的DRL架构及BDQ、Dueling DDQN模型，仿真较基准提升18.32%。
摘要：
本文提出一种深度强化学习解决方案，用于在3D空中高速公路上优化多无人机的小区关联决策及其移动速度。目标是提升运输和通信性能，包括避撞、连通性和切换。该问题被表述为马尔可夫决策过程（MDP），其中无人机状态由速度和通信数据速率定义。我们提出一种神经架构，包含共享决策模块和多个网络分支，每个分支专门处理二维运输-通信空间中的特定动作维度。该设计高效处理多维动作空间，允许各个动作维度保持独立。我们引入两个模型，即分支对决Q网络（BDQ）和分支对决双深度Q网络（Dueling DDQN），以展示该方法。仿真结果表明，与现有基准相比，其性能显著提升18.32%。
英文摘要：
This paper presents a deep reinforcement learning solution for optimizing multi-UAV cell-association decisions and their moving velocity on a 3D aerial highway. The objective is to enhance transportation and communication performance, including collision avoidance, connectivity, and handovers. The problem is formulated as a Markov decision process (MDP) with UAVs' states defined by velocities and communication data rates. We propose a neural architecture with a shared decision module and multiple network branches, each dedicated to a specific action dimension in a 2D transportation-communication space. This design efficiently handles the multi-dimensional action space, allowing independence for individual action dimensions. We introduce two models, Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN), to demonstrate the approach. Simulation results show a significant improvement of 18.32% compared to existing benchmarks.
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。
