Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.18246 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916022873751552 |
|---|---|
| author | Cong, Lin William Gan, Guangyan Qin, Hanzhang Yan, Zhenzhen |
| author_facet | Cong, Lin William Gan, Guangyan Qin, Hanzhang Yan, Zhenzhen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18246 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Privacy Preserving Reinforcement Learning with One-Sided Feedback Cong, Lin William Gan, Guangyan Qin, Hanzhang Yan, Zhenzhen Machine Learning Artificial Intelligence 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. |
| title | Privacy Preserving Reinforcement Learning with One-Sided Feedback |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2605.18246 |