{"type":"ScholarlyArticle","title":"Privacy-Preserving Reinforcement Learning with One-Sided Feedback","authors":[{"name":"Lin Cong","slug":"lin-cong","given":"Lin","family":"Cong","affiliation":""},{"name":"Guangyan Gan","slug":"guangyan-gan","given":"Guangyan","family":"Gan","affiliation":""},{"name":"Hanzhang Qin","slug":"hanzhang-qin","given":"Hanzhang","family":"Qin","affiliation":""},{"name":"Zhenzhen Yan","slug":"zhenzhen-yan","given":"Zhenzhen","family":"Yan","affiliation":""}],"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 of O~((1+E_rho) H^3 alpha^-2), which matches the known lower bounds for non-private RL. Here, E_rho denotes the privacy parameter, H is the time horizon, and alpha is 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.","doi":"10.24963/ijcai.2026/58","url":"https://ijcai.org/proceedings/2026/58","pdf":"https://ijcai.org/proceedings/2026/0058.pdf","markdown":"https://ijcai.org/proceedings/2026/58.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/58","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI Ethics, Trust, Fairnes","track":"Main Track","number":58,"pages":{"first":509,"last":517},"keywords":["Multidisciplinary Topics and Applications: Other","AI Ethics, Trust, Fairnes: Other","Machine Learning: Reinforcement learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}