Evaluating personalized, sequential treatment strategies for Alzheimer's disease (AD) using clinical trials is often impractical due to long disease horizons and substantial inter-patient heterogeneity. To address these constraints, we present the Alzheimer's Learning Platform for Adaptive Care Agents (ALPACA), an open-source, Gym-compatible reinforcement learning (RL) environment for systematically exploring personalized treatment strategies using existing therapies. ALPACA is powered by the Continuous Action-conditioned State Transitions (CAST) model trained on longitudinal trajectories from the Alzheimer's Disease Neuroimaging Initiative (ADNI), enabling medication-conditioned simulation of disease progression under alternative treatment decisions. We show that CAST autoregressively generates realistic medication-conditioned trajectories and that RL policies trained in ALPACA outperform no-treatment and behavior-cloned clinician baselines on memory-related outcomes. Interpretability analyses further indicated that the learned policies relied on clinically meaningful patient features when selecting actions. Overall, ALPACA provides a reusable in silico testbed for studying individualized sequential treatment decision-making for AD.

翻译：由于疾病周期漫长且患者间存在显著异质性，通过临床试验评估阿尔茨海默病（AD）的个性化序贯治疗策略往往难以实施。为应对这些限制，我们提出了阿尔茨海默病自适应护理智能体学习平台（ALPACA），这是一个开源且兼容Gym的强化学习（RL）环境，用于系统探索基于现有疗法的个性化治疗策略。ALPACA由基于阿尔茨海默病神经影像学倡议（ADNI）纵向轨迹训练的连续动作条件状态转移（CAST）模型驱动，能够模拟在不同治疗决策下药物条件化的疾病进展过程。我们证明CAST能够自回归地生成真实的药物条件化轨迹，并且在ALPACA中训练的强化学习策略在记忆相关结局指标上优于无治疗基线及行为克隆的临床医生基线。可解释性分析进一步表明，学习到的策略在选择行动时依赖于具有临床意义的患者特征。总体而言，ALPACA为研究AD的个体化序贯治疗决策提供了一个可重复使用的计算模拟测试平台。