RoutePPO: eBPF-Based Proximal Policy Optimization for Adaptive Routing in UAV Swarm Networks

Abstract

Unmanned Aerial Vehicle (UAV) swarm networks demand routing protocols that adapt continuously to rapid topology changes, node mobility, and fluctuating link quality. AODV may incur route-discovery overhead after topology changes, while OLSR relies on periodic topology dissemination that may lag behind fast link-quality changes; both can struggle under UAV swarm dynamics. We present RoutePPO, a closedloop adaptive routing framework that couples Proximal Policy Optimization (PPO) with eBPF-based real-time link telemetry and a P4 programmable data plane. RoutePPO-Adapt introduces a Top-K path encoder with fixed-order slot assignment and a 3-step slot-history observation, producing a topology-agnostic 30-dimensional state representation. Training uses a 9-scenario curriculum with anticipatory reward shaping and cosine learningrate decay. Across 15 deterministic routing scenarios, RoutePPOAdapt achieves a mean reward of 0.674–9.2% above the two-path baseline (RoutePPO-Base) - winning 11 of 15 scenarios while reducing latency by 31.6% and packet loss by 37.1%. A kernel-native evaluation (Linux netns + eBPF TC egress) confirms non-zero telemetry counters (0.13-0.27 Mbps), demonstrating end-to-end viability of the eBPF-PPO pipeline.