论文标题
单状态RMDP的梯度优化
Gradient Optimization for Single-State RMDPs
论文作者
论文摘要
随着诸如自主驾驶，机器人组件的控制和医学诊断之类的现代问题变得越来越难以分析解决，数据驱动的决策已经引起了很大的兴趣。如果存在比人们可以理解的复杂度更高的问题的问题，那么数据驱动的解决方案是一个强大的选择。这些方法中的许多属于称为强化学习的机器学习的细分。不幸的是，数据驱动的模型通常会在最坏情况下的表现不确定性。由于解决方案在分析上并未多次得出，因此这些模型将无法预测。在自动驾驶和医学等领域，这些失败的后果可能是灾难性的。 正在探索各种方法来解决此问题，其中之一被称为对抗性学习。它通过让一个模型优化其目标作为另一个模型目标的相反，将两个模型彼此相对应。这种类型的培训有可能找到在复杂和高赌注设置中可靠的模型，尽管不确定这种培训何时会起作用。目的是了解这些类型的模型何时达到稳定的解决方案。
As modern problems such as autonomous driving, control of robotic components, and medical diagnostics have become increasingly difficult to solve analytically, data-driven decision-making has seen a large gain in interest. Where there are problems with more dimensions of complexity than can be understood by people, data-driven solutions are a strong option. Many of these methods belong to a subdivision of machine learning known as reinforcement learning. Unfortunately, data-driven models often come with uncertainty in how they will perform in the worst of scenarios. Since the solutions are not derived analytically many times, these models will fail unpredictably. In fields such as autonomous driving and medicine, the consequences of these failures could be catastrophic. Various methods are being explored to resolve this issue and one of them is known as adversarial learning. It pits two models against each other by having one model optimize its goals as the opposite of the other model's goals. This type of training has the potential to find models which perform reliably in complex and high stakes settings, although it is not certain when this type of training will work. The goal is to gain insight about when these types of models will reach stable solutions.
