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arXiv · 2605.18246

Privacy Preserving Reinforcement Learning with One-Sided Feedback

Lin William Cong·Guangyan Gan·Hanzhang Qin·Zhenzhen Yan
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Abstract

We study reinforcement learning (RL) in multi-dimensional continuous state and action spaces with one-sided feedback, where the agent receives partial observations of the state and obtains reward information for only a subset of the state-action space at each time step. This setting introduces substantial challenges in both learning efficiency and privacy preservation. To address these challenges, we propose POOL, a novel privacy-preserving RL algorithm. We conduct a comprehensive theoretical analysis of POOL, deriving a sample complexity bound that matches the known lower bounds for non-private RL. Here, E_rho denotes the privacy parameter, H is the time horizon, and alpha is the optimality-gap parameter. Our findings show that it is possible to enforce strong privacy guarantees while maintaining high learning efficiency, marking a significant step toward practical, privacy-aware RL in multi-dimensional environments with one-sided feedback.

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Lin William Cong, Guangyan Gan, Hanzhang Qin, Zhenzhen Yan. 2026-05-18. Privacy Preserving Reinforcement Learning with One-Sided Feedback. https://arxiv.org/abs/2605.18246

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