Reinforcement Learning for Solving the Vehicle ... - NeurIPS

Reinforcement Learning for Solving theVehicle Routing ProblemMohammadreza Nazari Afshin Oroojlooy Martin Tak c Lawrence V. SnyderDepartment of Industrial and Systems EngineeringLehigh University, Bethlehem, PA present an end-to-end framework for Solving the Vehicle Routing Problem(VRP) using Reinforcement Learning . In this approach, we train a single policymodel that finds near-optimal solutions for a broad range of problem instances ofsimilar size, only by observing the reward signals and following feasibility consider a parameterized stochastic policy, and by applying a policy gradientalgorithm to optimize its parameters, the trained model produces the solution asa sequence of consecutive actions in real time, without the need to re-train forev

several classical combinatorial optimization problems such as TSP and the knapsack problem, they show the effectiveness and generality of their architecture. On a related topic, Dai et al. [11] solve optimization problems over graphs using a graph embedding structure [10] and a deep Q-learning (DQN) algorithm [26]. Even though VRP can be ...

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  Learning, Over, Reinforcement, Optimization, Combinatorial, Reinforcement learning, Combinatorial optimization

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