69 research outputs found
Decision-Oriented Learning with Differentiable Submodular Maximization for Vehicle Routing Problem
We study the problem of learning a function that maps context observations
(input) to parameters of a submodular function (output). Our motivating case
study is a specific type of vehicle routing problem, in which a team of
Unmanned Ground Vehicles (UGVs) can serve as mobile charging stations to
recharge a team of Unmanned Ground Vehicles (UAVs) that execute persistent
monitoring tasks. {We want to learn the mapping from observations of UAV task
routes and wind field to the parameters of a submodular objective function,
which describes the distribution of landing positions of the UAVs .}
Traditionally, such a learning problem is solved independently as a prediction
phase without considering the downstream task optimization phase. However, the
loss function used in prediction may be misaligned with our final goal, i.e., a
good routing decision. Good performance in the isolated prediction phase does
not necessarily lead to good decisions in the downstream routing task. In this
paper, we propose a framework that incorporates task optimization as a
differentiable layer in the prediction phase. Our framework allows end-to-end
training of the prediction model without using engineered intermediate loss
that is targeted only at the prediction performance. In the proposed framework,
task optimization (submodular maximization) is made differentiable by
introducing stochastic perturbations into deterministic algorithms (i.e.,
stochastic smoothing). We demonstrate the efficacy of the proposed framework
using synthetic data. Experimental results of the mobile charging station
routing problem show that the proposed framework can result in better routing
decisions, e.g. the average number of UAVs recharged increases, compared to the
prediction-optimization separate approach.Comment: camera-ready version for IROS 202
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