Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients

• URL: http://arxiv.org/abs/2012.15019v3
• Date: Tue, 16 Apr 2024 17:27:34 GMT
• Title: Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients
• Authors: Chris Cundy, Rishi Desai, Stefano Ermon,
• Abstract summary: We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions. We solve this problem by introducing a regularizer based on the mutual information between the sensitive state and the actions. We develop a model-based estimator for optimization of privacy-constrained policies.
• Score: 54.98496284653234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As reinforcement learning techniques are increasingly applied to real-world decision problems, attention has turned to how these algorithms use potentially sensitive information. We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions. We give examples of how this setting covers real-world problems in privacy for sequential decision-making. We solve this problem in the policy gradients framework by introducing a regularizer based on the mutual information (MI) between the sensitive state and the actions. We develop a model-based stochastic gradient estimator for optimization of privacy-constrained policies. We also discuss an alternative MI regularizer that serves as an upper bound to our main MI regularizer and can be optimized in a model-free setting, and a powerful direct estimator that can be used in an environment with differentiable dynamics. We contrast previous work in differentially-private RL to our mutual-information formulation of information disclosure. Experimental results show that our training method results in policies that hide the sensitive state, even in challenging high-dimensional tasks.

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