GraphRoute-Transfer: Topology-Generalizable Routing Convergence Optimization via Graph Reinforcement Learning

raphRoute -Transfer: Topology-eneralizable Routing Convergence Optimization via raph Reinforcement Learning

MARL-Converge: Decentralized Multi-Agent Reinforcement Learning for Failure-Aware Routing Convergence

Routing failures demand rapid control-plane convergence, yet uniformly aggressive detection and flooding increases signaling load and can induce unstable parameter oscillations. This paper presents MARL-Converge, a decentralized multi-agent reinforcement learning framework in which every router selects a local conservative, balanced, or aggressive control profile from router-local and one-hop telemetry. A parameter-shared dueling Double Deep Q-Network is trained with shared experience and a global-dominant team reward, while execution remains decentralized. A failure-aware action mask prevents routers without local evidence from blindly escalating to the aggressive profile. We implement a reproducible discrete-time link-state convergence proxy with independent topology views, stochastic failure detection and link-state advertisement diffusion, shortest-path recomputation, and forwarding-loop/drop checks. Evaluation comprises 600 matched episodes on the real Abilene and held-out AARNet topologies from the Internet Topology Zoo. Relative to a static balanced profile, MARL-Converge reduces convergence steps by 19.0% and modeled packet loss by 83.6%; the 6.1% overhead reduction is directionally favorable but not significant at the 0.05 level. Relative to a local heuristic, it is 9.3% faster, uses 14.0% fewer control units, and makes 56.4% fewer profile switches. It is 7.6% slower than always-aggressive control but cuts overhead by 35.4%. These results establish a measured speed-stability-efficiency trade-off without claiming physical-router timing equivalence.

Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks

This work proposes Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework that isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder.

TANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures

This work introduces TANGCO (Topology-Aware Neural Graph-Guided Capacity Optimization), which uses a graph neural network policy trained through the cascade simulator with policy-gradient learning and a heuristic anchor to allocate a fixed capacity budget across nodes to resist cascades under local load redistribution.

ConformalSafe-Routing: Uncertainty-Aware Safe Deep Reinforcement Learning for Adaptive Routing Convergence

Adaptive routing controllers based on deep reinforcement learning can shorten recovery after failures, but a policy optimized only for expected reward may select timer and damping configurations whose control overhead or route oscillation risk is poorly estimated under rare or shifted conditions. This paper presents ConformalSafe-Routing, a runtime safety layer for adaptive routing convergence. A dueling double deep Q-network ranks bounded routing profiles, while an independently trained risk model estimates one-step operational cost from topology, failure, load, and convergence-state features. Split conformal calibration converts point predictions into finite-sample upper bounds. A horizon-aware allocation uses a per-decision miscoverage budget of 0.0125 for an eight-step episode, and the shield selects the highest-value action whose upper bound satisfies the safety envelope; a balanced static profile is used when the certified set is empty. Experiments use four real telecom topologies from the Internet Topology Zoo and a fully released topology-driven event simulator. Across 300 paired episodes per condition, the proposed method reduces episode-level safety violations from 56.7% to 4.3% in-domain and from 64.0% to 2.0% under high-load distribution shift relative to unshielded DQN. It also reduces route flaps by 73.0% and 76.6%, respectively. Compared with a balanced static profile, it shortens mean convergence by 22.5% in-domain and 9.9% under shift while maintaining low violation rates. The results support conformal shielding as a practical mechanism for exposing and controlling the safety-speed trade-off in learning-based routing, while also identifying the limits of guarantees under topology and load shift.

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

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.