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The Ethereal
Differentially Private Policy Evaluation
March 07, 2016 ยท Declared Dead ยท ๐ International Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Borja Balle, Maziar Gomrokchi, Doina Precup
arXiv ID
1603.02010
Category
cs.LG: Machine Learning
Cross-listed
stat.ML
Citations
37
Venue
International Conference on Machine Learning
Last Checked
6 months ago
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.
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