A Mean Field Reinforcement Learning Approach to Large-Scale Vehicle Routing Problems

TMLR Paper5284 Authors

Abstract: Solving large-scale vehicle routing problems (VRPs) is NP-hard and poses a computational challenge in numerous applications such as logistics. Meanwhile, mean field control (MFC) provides a tractable and rigorous approach to controlling many agents. We provide a solution to pickup-and-delivery VRPs via scalable MFC. In combination with reinforcement learning (RL) and clustering, our MFC approach efficiently scales to large-scale VRPs. We perform a theoretical analysis of our MFC-based approximation, giving convergence results for large VRP instances and error bounds for clustering-based approximations. We verify our algorithms on different datasets and compare them against solutions such as OR-Tools, PyVRP and heuristics, showing scalability in terms of speed for mean-field methods, for the first time in discrete optimization. Overall, our work establishes a novel synthesis of MFC-based RL techniques, vehicle routing problems and clustering approximations, to solve a hard discrete optimization problem of practical use in a scalable way.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Xi_Lin2

Submission Number: 5284