Differentially Private Policy Evaluation
arXiv:1603.02010v138 citations
Originality Highly original
AI Analysis
This work addresses privacy concerns in reinforcement learning for applications like healthcare or finance, representing a foundational step rather than an incremental improvement.
The paper tackles the problem of evaluating reinforcement learning policies while preserving privacy, introducing the first differentially private algorithms for this task and demonstrating promising results on simple examples.
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.