Confounding-robust policy improvement的主要方法

Confounding-robust policy improvement 的主要方法是通过考虑混淆因素来提高策略的鲁棒性。在强化学习中，混淆因素是指与目标变量相关的变量，但不应该被纳入策略优化的考虑范围内，因为它们与环境的真实因果关系不同。具体来说，该方法提出了一种基于反事实学习的算法，通过生成反事实数据来估计混淆因素对策略性能的影响。然后，该方法使用生成的反事实数据来训练一个混淆因素模型，该模型可以预测混淆因素对策略的影响。最后，该方法将混淆因素模型与强化学习策略一起优化，从而提高策略的鲁棒性。通过这种方法，Confounding-robust policy improvement 可以在考虑混淆因素的情况下优化策略，从而使得策略在真实环境中表现更好。

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post-hoc analysis

Post-hoc analysis refers to the analysis of data after an experiment, study, or survey has been conducted. It involves examining the data to identify patterns, trends, and relationships that were not initially anticipated or observed during the initial analysis. Post-hoc analysis is often used to explore the underlying factors that may have influenced the results of a study or experiment, and to identify potential confounding variables that may have affected the outcomes. It is important to note that post-hoc analysis is exploratory in nature and should be interpreted with caution, as it may produce false positive results or lead to overinterpretation of the data.

Please revise the paper：Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

回答：本文提出了一种基于底部类型自适应的卫星深度测量（BA-SDB）方法，以解决传统卫星深度测量模型中假设底部均匀的局限性以及不能充分考虑影响反射率的干扰因素，如浊度等。通过基于聚类的底部反射率自适应分割，并采用PSO-LightGBM算法考虑多种因素，实现了对不同沉积物和水深的准确深度估算，与空中激光测深和多波束回声深度声纳联合实测结果相比，均方根误差值达到0.85m，是传统比值法、多波段法和机器学习方法中最准确的方法。