Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this paper, we propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings.

翻译：基于证据或数据驱动的动态治疗方案对于个性化医疗至关重要，而离线强化学习（RL）可为此提供支持。尽管各医疗机构拥有海量医疗数据，但由于隐私保护限制，这些数据无法共享。此外，不同机构间存在异质性。因此，联邦离线强化学习算法对于解决这些问题是必要且具有前景的。本文提出了一种多站点马尔可夫决策过程模型，该模型可同时考虑站点间的同质性与异质性效应，并实现了对站点层面特征的分析。我们设计了首个具有样本复杂度的离线联邦策略优化算法。该算法通信高效，仅需通过交换汇总统计信息进行单轮通信交互。我们为该算法提供了理论保证，其学习策略的次优性可与数据非分布式场景下的收敛速率相媲美。大量仿真实验验证了所提算法的有效性。最后，将该方法应用于多站点脓毒症数据集，以说明其在临床场景中的应用价值。