构建模拟环境，助力强化学习优化运动干预策略
StepCountJITAI: simulation environment for RL with application to physical activity adaptive intervention
- 设计专用仿真环境StepCountJITAI，模拟真实运动干预场景
- 解决真实数据少、环境动态不贴合的问题，提升算法适用性
- 适合研究移动健康与自适应干预的学者和开发者
将强化学习（RL）用于即时自适应干预（JITAIs）政策学习在改善体力活动等行为干预领域备受关注。在基于消息的体力活动JITAI中，移动健康应用通过发送信息鼓励用户参与运动，而RL可用于学习在不同情境下提供何种干预选项。然而，真实干预研究受限于成本和时间，数据量有限，难以训练有效策略。此外，现有常用RL仿真环境的动态与体力活动干预关联度低，难以揭示最优算法。本文提出StepCountJITAI，一个专为体力活动自适应干预设计的强化学习仿真环境，旨在推动针对该挑战性领域的政策学习方法研究。
原文摘要 · Abstract (English)
The use of reinforcement learning (RL) to learn policies for just-in-time adaptive interventions (JITAIs) is of significant interest in many behavioral intervention domains including improving levels of physical activity. In a messaging-based physical activity JITAI, a mobile health app is typically used to send messages to a participant to encourage engagement in physical activity. In this setting, RL methods can be used to learn what intervention options to provide to a participant in different contexts. However, deploying RL methods in real physical activity adaptive interventions comes with challenges: the cost and time constraints of real intervention studies result in limited data to learn adaptive intervention policies. Further, commonly used RL simulation environments have dynamics that are of limited relevance to physical activity adaptive interventions and thus shed little light on what RL methods may be optimal for this challenging application domain. In this paper, we introduce StepCountJITAI, an RL environment designed to foster research on RL methods that address the significant challenges of policy learning for adaptive behavioral interventions.
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