Year: 2016
Type: preprint
Abstract: We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy. We establish two approaches for achieving differential privacy, provide a theoretical analysis of the privacy and utility of the two algorithms, and show promising results on simple empirical examples.
Source: arXiv (Cornell University)
Authors: Balle, Borja, Gomrokchi, Maziar, Precup, Doina
Institutions: Lancaster University, McGill University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2294521904