评估上下文推断错误和部分可观察性对RL方法对即时适应性干预的影响.
Karine Karine1, Predrag Klasnja2, Susan A Murphy3
1University of Massachusetts Amherst.
概括
强化学习可以通过学习最佳支持序列来优化个性化健康干预 (JITAI). 当环境不确定时,传播不确定性是有效性的关键,政策梯度很好地处理局部状态信息.
科学领域:
- 行为科学是一种行为科学.
- 机器学习是机器学习.
- 数字健康数字健康
背景情况:
- 及时适应性干预 (JITAI) 通过根据用户状态选择干预组件来提供个性化的支持.
- 优化JITAI需要学习有效的政策来选择干预选项.
研究的目的:
- 探索强化学习 (RL) 用于学习JITAI的政策.
- 调查上下文推断错误和部分可观察性对政策学习的影响.
主要方法:
- 强化学习算法的应用到JITAI的政策学习.
- 对上下文推断错误和部分可观测效应的分析.
主要成果:
- 从上下文推断中传播不确定性对于在增加的上下文不确定性下提高JITAI有效性至关重要.
- 政策梯度算法证明了对部分观察到的行为状态信息的稳定性.
结论:
- 在优化JITAI方面,RL方法是有前途的.
- 在上下文推断中管理不确定性和利用强大的算法是有效的适应性干预的关键.
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