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Most real-world optimization problems have multiple objectives. A systemdesigner needs to find a policy that trades off these objectives to reach adesired operating point. This problem has been studied extensively in thesetting of known objective functions. We consider a more practical butchallenging setting of unknown objective functions. In industry, this problemis mostly approached with online A/B testing, which is often costly andinefficient. As an alternative, we propose interactive multi-objectiveoff-policy optimization (IMO$^3$). The key idea in our approach is to interactwith a system designer using policies evaluated in an off-policy fashion touncover which policy maximizes her unknown utility function. We theoreticallyshow that IMO$^3$ identifies a near-optimal policy with high probability,depending on the amount of feedback from the designer and training data foroff-policy estimation. We demonstrate its effectiveness empirically on multiplemulti-objective optimization problems.

IMO$^3$: Interactive Multi-Objective Off-Policy Optimization