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Title: Near-Optimal Differentially Private Reinforcement Learning
Motivated by personalized healthcare and other applications involving sensitive data, we study online exploration in reinforcement learning with differential privacy (DP) constraints. Existing work on this problem established that no-regret learning is possible under joint differential privacy (JDP) and local differential privacy (LDP) but did not provide an algorithm with optimal regret. We close this gap for the JDP case by designing an $\epsilon$-JDP algorithm with a regret of $\widetilde{O}(\sqrt{SAH^2T}+S^2AH^3/\epsilon)$ which matches the information-theoretic lower bound of non-private learning for all choices of $\epsilon> S^{1.5}A^{0.5} H^2/\sqrt{T}$. In the above, $S$, $A$ denote the number of states and actions, $H$ denotes the planning horizon, and $T$ is the number of steps. To the best of our knowledge, this is the first private RL algorithm that achieves privacy for free asymptotically as $T\rightarrow \infty$. Our techniques — which could be of independent interest — include privately releasing Bernstein-type exploration bonuses and an improved method for releasing visitation statistics. The same techniques also imply a slightly improved regret bound for the LDP case.  more » « less
Award ID(s):
2007117
NSF-PAR ID:
10466932
Author(s) / Creator(s):
;
Editor(s):
Ruiz, Francisco and
Publisher / Repository:
PMLR
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
206
ISSN:
2640-3498
Page Range / eLocation ID:
9914--9940
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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