Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback

• URL: http://arxiv.org/abs/2302.03805v2
• Date: Wed, 1 Nov 2023 03:06:11 GMT
• Title: Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
• Authors: Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew R. Walter
• Abstract summary: We propose a multi-objective decision making framework that accommodates different user preferences over objectives. Our model consists of a Markov decision process with a vector-valued reward function, with each user having an unknown preference vector. We suggest an algorithm that finds a nearly optimal policy for the user using a small number of comparison queries.
• Score: 76.7007545844273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In classic reinforcement learning (RL) and decision making problems, policies are evaluated with respect to a scalar reward function, and all optimal policies are the same with regards to their expected return. However, many real-world problems involve balancing multiple, sometimes conflicting, objectives whose relative priority will vary according to the preferences of each user. Consequently, a policy that is optimal for one user might be sub-optimal for another. In this work, we propose a multi-objective decision making framework that accommodates different user preferences over objectives, where preferences are learned via policy comparisons. Our model consists of a Markov decision process with a vector-valued reward function, with each user having an unknown preference vector that expresses the relative importance of each objective. The goal is to efficiently compute a near-optimal policy for a given user. We consider two user feedback models. We first address the case where a user is provided with two policies and returns their preferred policy as feedback. We then move to a different user feedback model, where a user is instead provided with two small weighted sets of representative trajectories and selects the preferred one. In both cases, we suggest an algorithm that finds a nearly optimal policy for the user using a small number of comparison queries.

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