Risk‐Aware Route Optimization Using Minimum Spanning Tree Initialization and a Transferable Reinforcement Learning Policy

Efficient route planning is a central challenge in applications such as autonomous area monitoring, infrastructure inspection, and logistics, where an agent must visit a set of locations while accounting not only for travel distance but also for spatially varying traversal risk. Classical distance‐only optimizers minimize path length but are blind to such nongeometric costs. We propose a hybrid framework that integrates minimum spanning tree (MST)–based initialization with a transferable reinforcement‐learning (RL) refinement policy for risk‐aware routing. The MST provides a structured, low‐cost initial tour; the RL agent then refines it under a multicriteria cost that combines distance and risk exposure. The key design choice is that the policy’s action‐value function is linear in instance‐independent, size‐normalized features of a candidate edge exchange. As a result, a single 10‐parameter weight vector is trained once and deployed on unseen instances and unseen problem sizes without any per‐instance retraining. Across instances of 15–100 nodes (30 independent instances per size; 12 at n  = 100), the proposed MST–RL method reduces multicriteria cost by 16%–27% over the MST baseline ( p < 10 ⁻³ at every size) and decisively outperforms a randomly initialized RL policy (up to 44% at n  = 100, p < 10 ⁻³ ), confirming the value of MST initialization. It attains cost competitive with, though not superior to, exhaustive local search and ant colony optimization while running 24–126 × faster per instance at n  = 100, with inference time that does not grow with problem size. A reward‐weight sweep demonstrates controllable distance–risk trade‐offs. These properties make the method attractive for on‐board, real‐time deployment on resource‐constrained hardware.