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arXiv · 1911.00638

Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints

Samuel Daulton·Shaun Singh·Vashist Avadhanula·Drew Dimmery·Eytan Bakshy
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Abstract

Recent advances in contextual bandit optimization and reinforcement learning have garnered interest in applying these methods to real-world sequential decision making problems. Real-world applications frequently have constraints with respect to a currently deployed policy. Many of the existing constraint-aware algorithms consider problems with a single objective (the reward) and a constraint on the reward with respect to a baseline policy. However, many important applications involve multiple competing objectives and auxiliary constraints. In this paper, we propose a novel Thompson sampling algorithm for multi-outcome contextual bandit problems with auxiliary constraints. We empirically evaluate our algorithm on a synthetic problem. Lastly, we apply our method to a real world video transcoding problem and provide a practical way for navigating the trade-off between safety and performance using Bayesian optimization.

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Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy. 2019-11-02. Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints. https://arxiv.org/abs/1911.00638

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