Learning Differentially Private Rewards from Human Feedback

Ezgi Korkmaz, Jonah Brown-Cohen

Primary Area: learning theory

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Keywords: Learning to Rank, Differential Privacy, Minimax Optimal

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Abstract: We study the privacy of reinforcement learning from human feedback. In particular, we focus on solving the problem of reinforcement learning from preference rankings, subject to the constraint of differential privacy, in MDPs where true rewards are given by linear functions. To achieve this, we analyze $(\epsilon,\delta)$-differential privacy (DP) for both the Bradley-Terry-Luce (BTL) model and the Plackett-Luce (PL) model. We provide a differentially private algorithm for learning rewards from human rankings. We further show that the privately learned rewards can be used to train policies achieving statistical performance guarantees that asymptotically match the best known algorithms in the non-private setting, which are in some cases minimax optimal.

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Submission Number: 7653