2025 |
Jihoon Suh / Yeongjun Jang / Kaoru Teranishi / Takashi Tanaka Relative entropy regularized reinforcement learning for efficient encrypted policy synthesis Journal Article In: IEEE Control Systems Letters, vol. 9, 2025, ISSN: 2475-1456. Abstract | Links | BibTeX | Tags: Entropy Regularization, Homomorphic encryption, Reinforcement learning @article{nokey,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. |
List of English Publication
2025 |
Relative entropy regularized reinforcement learning for efficient encrypted policy synthesis Journal Article In: IEEE Control Systems Letters, vol. 9, 2025, ISSN: 2475-1456. |