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Title: IMO^3: Interactive Multi-Objective Off-Policy Optimization
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. However, we consider a more practical but challenging setting of unknown objective functions. In industry, optimization under this setting 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^3). The key idea of IMO^3 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^3 identifies a near-optimal policy with high probability, depending on the amount of designer's feedback and training data for off-policy estimation. We demonstrate its effectiveness empirically on several multi-objective optimization problems.  more » « less
Award ID(s):
2128019 2007492 1553568
NSF-PAR ID:
10381231
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22
Page Range / eLocation ID:
3523 to 3529
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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