Related papers: Differentially Private Exploration in Reinforcement Learning with Linear Representation

Differentially Private Exploration in Reinforcement Learning with Linear Representation

URL: http://arxiv.org/abs/2112.01585v1
Date: Thu, 2 Dec 2021 19:59:50 GMT
Title: Differentially Private Exploration in Reinforcement Learning with Linear Representation
Authors: Paul Luyo and Evrard Garcelon and Alessandro Lazaric and Matteo Pirotta
Abstract summary: We first consider the setting of linear-mixture MDPs (Ayoub et al., 2020) (a.k.a. model-based setting) and provide an unified framework for analyzing joint and local differential private (DP) exploration. We further study privacy-preserving exploration in linear MDPs (Jin et al., 2020) (a.k.a. model-free setting) where we provide a $widetildeO(sqrtK/epsilon)$ regret bound for $(epsilon,delta)
Score: 102.17246636801649
License: http://creativecommons.org/licenses/by/4.0/
Abstract: This paper studies privacy-preserving exploration in Markov Decision Processes (MDPs) with linear representation. We first consider the setting of linear-mixture MDPs (Ayoub et al., 2020) (a.k.a.\ model-based setting) and provide an unified framework for analyzing joint and local differential private (DP) exploration. Through this framework, we prove a $\widetilde{O}(K^{3/4}/\sqrt{\epsilon})$ regret bound for $(\epsilon,\delta)$-local DP exploration and a $\widetilde{O}(\sqrt{K/\epsilon})$ regret bound for $(\epsilon,\delta)$-joint DP. We further study privacy-preserving exploration in linear MDPs (Jin et al., 2020) (a.k.a.\ model-free setting) where we provide a $\widetilde{O}(\sqrt{K/\epsilon})$ regret bound for $(\epsilon,\delta)$-joint DP, with a novel algorithm based on low-switching. Finally, we provide insights into the issues of designing local DP algorithms in this model-free setting.

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