论文标题
快速个性化的移动健康治疗政策有限的数据
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
论文作者
论文摘要
在移动健康（MHealth）中，在不学习个性化政策的情况下适应环境的强化学习算法可能无法区分个人的需求。然而，由于现场交付MHealth干预措施引起的大量噪音可能会削弱算法在仅访问单个用户数据时学习的能力，从而使个性化具有挑战性。我们提出智能泵，该智能通过自适应，原则地使用其他用户的数据来学习个性化的策略。我们表明，在所有生成模型中，智能泵的平均遗憾平均比最先进的遗憾低26％。此外，我们在实时临床试验中检查了这种方法的行为，证明了它甚至可以向一小群用户学习的能力。
In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of individuals. Yet the high amount of noise due to the in situ delivery of mHealth interventions can cripple the ability of an algorithm to learn when given access to only a single user's data, making personalization challenging. We present IntelligentPooling, which learns personalized policies via an adaptive, principled use of other users' data. We show that IntelligentPooling achieves an average of 26% lower regret than state-of-the-art across all generative models. Additionally, we inspect the behavior of this approach in a live clinical trial, demonstrating its ability to learn from even a small group of users.
