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arXiv · 2506.12358

Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis

Jihoon Suh·Yeongjun Jang·Kaoru Teranishi·Takashi Tanaka
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Abstract

We propose an efficient encrypted policy synthesis to develop privacy-preserving model-based reinforcement learning. We first demonstrate that the relative-entropy-regularized reinforcement learning framework offers a computationally convenient linear and ``min-free'' structure for value iteration, enabling a direct and efficient integration of fully homomorphic encryption with bootstrapping into policy synthesis. Convergence and error bounds are analyzed as encrypted policy synthesis propagates errors under the presence of encryption-induced errors including quantization and bootstrapping. Theoretical analysis is validated by numerical simulations. Results demonstrate the effectiveness of the RERL framework in integrating FHE for encrypted policy synthesis.

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Jihoon Suh, Yeongjun Jang, Kaoru Teranishi, Takashi Tanaka. 2025-06-14. Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis. https://doi.org/10.1109/lcsys.2025.3578573

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