Learning controllers with offline data in decision-making systems is an essential area of research due to its potential to reduce the risk of applications in real-world systems. However, in responsibility-sensitive settings such as healthcare, decision accountability is of paramount importance, yet has not been adequately addressed by the literature. This paper introduces the Accountable Offline Controller (AOC) that employs the offline dataset as the Decision Corpus and performs accountable control based on a tailored selection of examples, referred to as the Corpus Subset. AOC operates effectively in low-data scenarios, can be extended to the strictly offline imitation setting, and displays qualities of both conservation and adaptability. We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high levels of performance while maintaining accountability.

翻译：在决策系统中利用离线数据学习控制器是一个重要的研究领域，因其具有降低现实系统应用风险的潜力。然而，在医疗保健等责任敏感型环境中，决策可问责性至关重要，但现有文献尚未充分解决这一问题。本文提出可问责离线控制器（AOC），该控制器将离线数据集作为决策语料库，并基于精心挑选的示例子集（称为语料库子集）执行可问责控制。AOC在低数据场景下仍能有效运行，可扩展至严格离线模仿学习场景，并兼具保守性与适应性。我们在模拟环境和真实医疗场景中评估AOC的性能，重点展示了其在保持可问责性的同时，高效完成离线控制任务的能力。