Actor Critic with Differentially Private Critic

Jonathan Lebensold; William Hamilton; Borja Balle; Doina Precup

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arXiv · 1910.05876

Actor Critic with Differentially Private Critic

Jonathan Lebensold·William Hamilton·Borja Balle·Doina Precup

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Abstract

Reinforcement learning algorithms are known to be sample inefficient, and often performance on one task can be substantially improved by leveraging information (e.g., via pre-training) on other related tasks. In this work, we propose a technique to achieve such knowledge transfer in cases where agent trajectories contain sensitive or private information, such as in the healthcare domain. Our approach leverages a differentially private policy evaluation algorithm to initialize an actor-critic model and improve the effectiveness of learning in downstream tasks. We empirically show this technique increases sample efficiency in resource-constrained control problems while preserving the privacy of trajectories collected in an upstream task.

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Jonathan Lebensold, William Hamilton, Borja Balle, Doina Precup. 2019-10-14. Actor Critic with Differentially Private Critic. https://arxiv.org/abs/1910.05876

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