基于核的分布式Q学习：一种面向动态治疗方案的可扩展强化学习方法
Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes
AI总结：
针对利用大规模电子健康记录构建动态治疗方案的问题，提出一种可扩展的基于核的分布式Q学习算法，在显著降低深度强化学习计算复杂度的同时保持相当的累积奖励泛化性能。
摘要：
近年来，大量与慢性病相关的电子健康记录（EHRs）被收集起来，以促进医疗诊断。利用动态治疗方案（DTRs）可以高效地对EHRs中与慢性病相关的动态特性进行建模。尽管强化学习（RL）是构建DTRs的一种广泛使用的方法，但开发能够有效处理大量数据的RL算法仍是持续研究的方向。本文提出了一种可扩展的、基于核的分布式Q学习算法，用于生成DTRs。我们对所提方法进行了理论评估和数值分析。结果表明，与最先进的深度强化学习方法相关的计算复杂度相比，我们的算法显著降低了计算复杂度，同时在跨阶段累积奖励（例如生存时间或累积生存概率）方面保持了相当的泛化性能。
英文摘要：
In recent years, large amounts of electronic health records (EHRs) concerning chronic diseases have been collected to facilitate medical diagnosis. Modeling the dynamic properties of EHRs related to chronic diseases can be efficiently done using dynamic treatment regimes (DTRs). While reinforcement learning (RL) is a widely used method for creating DTRs, there is ongoing research in developing RL algorithms that can effectively handle large amounts of data. In this paper, we present a scalable kernel-based distributed Q-learning algorithm for generating DTRs. We perform both theoretical assessments and numerical analysis for the proposed approach. The results demonstrate that our algorithm significantly reduces the computational complexity associated with the state-of-the-art deep reinforcement learning methods, while maintaining comparable generalization performance in terms of accumulated rewards across stages, such as survival time or cumulative survival probability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
