用强化学习联合优化无人机飞行与路由，提升延迟容忍网络的传输效率。
Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks
- 通过强化学习协同控制无人机航向与节点间消息复制策略。
- 在四种流量模式下，交付率比PRoPHET和MaxProp提升15%-28%。
- 适合研究无人机网络、智能路由及分布式强化学习的读者。
随着延迟容忍网络（DTNs）的广泛应用，存储-携带-转发（SCF）通信在稀疏连接环境下不可或缺。然而，间歇性连接、有限缓冲区及消息生存时间（TTL）限制常导致交付稀疏和拥塞，严重降低端到端性能。为此，本文探索了去中心化机会路由与可控制无人机飞行的联合优化，旨在通过离散无人机航向扩大未来接触机会，并在连接受限观测下实现节点级消息复制。基于此架构，我们提出合作因子路由——无人机控制采用集中训练、去中心化执行（CTDE），并设计了基于近端策略优化（PPO）的JUROR框架。首先将问题建模为带序贯运动-路由耦合的因子部分可观马尔可夫决策过程，采用每步团队奖励；训练时，各智能体基于局部观测行动，而批评者使用全局统计信息；此外可选多时域热点预测器提供辅助监督。仿真在四种交通模式下验证了有效性能提升，优于PRoPHET与MaxProp，同时保持连接受限下的去中心化执行。
原文摘要 · Abstract (English)
The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity. However, intermittent contacts, finite buffers, and limited message time-to-live (TTL) often give rise to sparse delivery and congestion, leading to substantial end-to-end performance degradation. To address this challenge, this study explores the joint optimization of decentralized opportunistic routing and controllable unmanned aerial vehicle (UAV) flight, aiming to enlarge future contacts through discrete UAV headings while enabling per-node replication under contact-limited observations. Building upon this architecture, we study cooperative factored routing--UAV control under centralized training and decentralized execution (CTDE) and propose JUROR (Joint UAV flight and Opportunistic Routing, based on the proximal policy optimization (PPO) framework. In our design, we first cast the problem as a factored partially observable Markov decision process with sequential motion--routing coupling and a per-step team reward; subsequently, decentralized actors act on local observations while a training-time critic uses global statistics, and an optional multi-horizon hotspot predictor provides auxiliary supervision. Simulation results over four traffic modes demonstrate effective gains over PRoPHET and MaxProp, while retaining contact-limited decentralized execution.
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