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arXiv AI By Abid Mohamed Nadhir, Ahmad Al Hanbali, Beggas Mounir

Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints

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The paper introduces the Homomorphic Advantage Operator (HAO), a stabilization framework for reinforcement learning under fully homomorphic encryption (FHE). HAO applies a zero‑mean centering projection to temporal‑difference targets, eliminating the uniform state‑value baseline that causes the Bellman drift and keeping network pre‑activations within safe polynomial approximation bounds. Experiments on tabular MDPs, an encrypted CartPole environment, and a logistics routing benchmark show that HAO prevents boundary breaches entirely, improves optimal policy accuracy by 18 percentage points, and remains stable even with added Gaussian noise.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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