增强塑性性多智能体专家混合模型用于无人机辅助应急通信网络动态目标适应
Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks
AI总结：
本文提出PE-MAMoE模型，通过增强塑性机制提升无人机在动态目标适应中的性能，实验显示其在用户容量、碰撞减少等方面有显著提升。
摘要：
无人机作为空中基站可快速恢复灾害后连接，但用户移动和交通需求的突变导致服务质量权衡和强非平稳性。深度强化学习在突变下易出现塑性损失，表现为表示崩溃和神经元休眠。本文提出增强塑性多智能体专家混合模型（PE-MAMoE），基于多智能体近端策略优化框架。PE-MAMoE为每个无人机配备稀疏门控专家混合演员，其路由器每步选择一个专家。非参数化相位控制器在相位切换后注入短暂专家专属随机扰动，重置动作日志标准差，退火熵和学习率，调度路由温度，以重新塑形策略而不破坏安全行为。推导出动态遗憾界，显示跟踪误差与环境变化和累积噪声能量成正比。在相位驱动模拟器中，PE-MAMoE在用户容量、碰撞减少等方面优于基线模型。诊断确认在制度切换时专家特征排名更高且存在周期性神经元恢复。
英文摘要：
Unmanned aerial vehicles serving as aerial base stations can rapidly restore connectivity after disasters, yet abrupt changes in user mobility and traffic demands shift the quality of service trade-offs and induce strong non-stationarity. Deep reinforcement learning policies suffer from plasticity loss under such shifts, as representation collapse and neuron dormancy impair adaptation. We propose plasticity enhanced multi-agent mixture of experts (PE-MAMoE), a centralized training with decentralized execution framework built on multi-agent proximal policy optimization. PE-MAMoE equips each UAV with a sparsely gated mixture of experts actor whose router selects a single specialist per step. A non-parametric Phase Controller injects brief, expert-only stochastic perturbations after phase switches, resets the action log-standard-deviation, anneals entropy and learning rate, and schedules the router temperature, all to re-plasticize the policy without destabilizing safe behaviors. We derive a dynamic regret bound showing the tracking error scales with both environment variation and cumulative noise energy. In a phase-driven simulator with mobile users and 3GPP-style channels, PE-MAMoE improves normalized interquartile mean return by 26.3\% over the best baseline, increases served-user capacity by 12.8\%, and reduces collisions by approximately 75\%. Diagnostics confirm persistently higher expert feature rank and periodic dormant-neuron recovery at regime switches.
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。
