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arXiv · 2010.12001

Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

Arthur Delarue·Ross Anderson·Christian Tjandraatmadja
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Abstract

Value-function-based methods have long played an important role in reinforcement learning. However, finding the best next action given a value function of arbitrary complexity is nontrivial when the action space is too large for enumeration. We develop a framework for value-function-based deep reinforcement learning with a combinatorial action space, in which the action selection problem is explicitly formulated as a mixed-integer optimization problem. As a motivating example, we present an application of this framework to the capacitated vehicle routing problem (CVRP), a combinatorial optimization problem in which a set of locations must be covered by a single vehicle with limited capacity. On each instance, we model an action as the construction of a single route, and consider a deterministic policy which is improved through a simple policy iteration algorithm. Our approach is competitive with other reinforcement learning methods and achieves an average gap of 1.7% with state-of-the-art OR methods on standard library instances of medium size.

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BibTeXRIS

Arthur Delarue, Ross Anderson, Christian Tjandraatmadja. 2020-10-22. Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing. https://arxiv.org/abs/2010.12001

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