从噪声样本数据集综合控制策略：概率方法
On the synthesis of control policies from noisy example datasets: a probabilistic approach
摘要：
在这篇笔记中，我们考虑的问题，综合最优控制政策的系统从噪声数据集。我们提出了一种新的算法，作为输入的可用数据集，并根据这些输入，计算可能的随机和非线性系统，也满足驱动约束的最优策略。该算法依赖于坚实的理论基础，其关键根源是动力系统的概率解释。我们的方法的有效性是通过考虑自动驾驶汽车用例来说明的。对于这样的用例，我们利用我们的算法从噪声数据中合成一个控制策略，允许汽车合并到交叉口，同时满足对汽车速度方差的附加约束
英文摘要：
In this note we consider the problem of synthesizing optimal control policies for a system from noisy datasets. We present a novel algorithm that takes as input the available dataset and, based on these inputs, computes an optimal policy for possibly stochastic and nonlinear systems that also satisfies actuation constraints. The algorithm relies on solid theoretical foundations, which have their key roots into a probabilistic interpretation of dynamical systems. The effectiveness of our approach is illustrated by considering an autonomous car use case. For such use case, we make use of our algorithm to synthesize a control policy from noisy data allowing the car to merge onto an intersection, while satisfying additional constraints on the variance of the car speed
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
