Suboptimal health related behaviors and habits; and resulting chronic diseases are responsible for majority of deaths globally. Studies show that providing personalized support to patients yield improved results by preventing and/or timely treatment of these problems. Digital, just-in-time and adaptive interventions are mobile phone-based notifications that are being utilized to support people wherever and whenever necessary in coping with their health problems. In this research, we propose a reinforcement learning-based mechanism to personalize interventions in terms of timing, frequency and preferred type(s). We simultaneously employ two reinforcement learning models, namely intervention-selection and opportune-moment-identification; capturing and exploiting changes in people's long-term and momentary contexts respectively. While the intervention-selection model adapts the intervention delivery with respect to type and frequency, the opportune-moment-identification model tries to find the most opportune moments to deliver interventions throughout a day. We propose two accelerator techniques over the standard reinforcement learning algorithms to boost learning performance. First, we propose a customized version of eligibility traces for rewarding past actions throughout an agent's trajectory. Second, we utilize the transfer learning method to reuse knowledge across multiple learning environments. We validate the proposed approach in a simulated experiment where we simulate four personas differing in their daily activities, preferences on specific intervention types and attitudes towards the targeted behavior. Our experiments show that the proposed approach yields better results compared to the standard reinforcement learning algorithms and successfully capture the simulated variations associated with the personas.

与健康相关的不良行为和习惯；由此产生的慢性病是全球大部分死亡的原因。研究表明，通过预防和/或及时治疗这些问题，为患者提供个性化支持会产生更好的结果。数字化、即时和自适应干预是基于手机的通知，用于支持人们随时随地应对健康问题。在这项研究中，我们提出了一种基于强化学习的机制，可以在时间、频率和首选类型方面个性化干预。我们同时采用两种强化学习模型，即干预选择和时机识别; 分别捕捉和利用人们长期和瞬时环境的变化。干预选择模型根据类型和频率调整干预实施，而时机识别模型则试图找到最合适的时刻来实施全天的干预。我们在标准强化学习算法上提出了两种加速器技术，以提高学习性能。首先，我们提出了一个定制版本的资格痕迹，用于奖励整个智能体轨迹中过去的动作。其次，我们利用迁移学习方法在多个学习环境中重用知识。我们在模拟实验中验证了所提出的方法，我们模拟了四个在日常活动中不同的角色，对特定干预类型的偏好和对目标行为的态度。我们的实验表明，与标准强化学习算法相比，所提出的方法产生了更好的结果，并成功捕获了与角色相关的模拟变化。