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Private Reinforcement Learning with PAC and Regret Guarantees

Giuseppe Vietri · Borja de Balle Pigem · Akshay Krishnamurthy · Steven Wu


Keywords: [ Privacy-preserving Statistics and Machine Learning ] [ Reinforcement Learning ] [ Reinforcement Learning - General ]


Motivated by high-stakes decision-making domains like personalized medicine where user information is inherently sensitive, we design privacy preserving exploration policies for episodic reinforcement learning (RL). We first provide a meaningful privacy formulation using the notion of joint differential privacy (JDP)--a strong variant of differential privacy for settings where each user receives their own sets of output (e.g., policy recommendations). We then develop a private optimism-based learning algorithm that simultaneously achieves strong PAC and regret bounds, and enjoys a JDP guarantee. Our algorithm only pays for a moderate privacy cost on exploration: in comparison to the non-private bounds, the privacy parameter only appears in lower-order terms. Finally, we present lower bounds on sample complexity and regret for reinforcement learning subject to JDP.

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