IMO$^3$: Interactive Multi-Objective Off-Policy Optimization
Abstract
Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. We consider a more practical but challenging setting of unknown objective functions. In industry, this problem is mostly approached with online A/B testing, which is often costly and inefficient. As an alternative, we propose interactive multi-objective off-policy optimization (IMO). The key idea in our approach is to interact with a system designer using policies evaluated in an off-policy fashion to uncover which policy maximizes her unknown utility function. We theoretically show that IMO identifies a near-optimal policy with high probability, depending on the amount of feedback from the designer and training data for off-policy estimation. We demonstrate its effectiveness empirically on multiple multi-objective optimization problems.
Cite
@article{arxiv.2201.09798,
title = {IMO$^3$: Interactive Multi-Objective Off-Policy Optimization},
author = {Nan Wang and Hongning Wang and Maryam Karimzadehgan and Branislav Kveton and Craig Boutilier},
journal= {arXiv preprint arXiv:2201.09798},
year = {2022}
}