论文标题
关于嘈杂示例数据集的控制策略的合成：一种概率方法
On the synthesis of control policies from noisy example datasets: a probabilistic approach
论文作者
论文摘要
在本说明中，我们考虑了从嘈杂数据集合成系统的最佳控制策略的问题。我们提出了一种新颖的算法，该算法将可用数据集作为输入，并基于这些输入为可能的随机和非线性系统计算最佳策略，该策略也满足了驱动约束。该算法依赖于固体理论基础，其主要根源是对动态系统的概率解释。通过考虑一种自动驾驶汽车用例来说明我们方法的有效性。对于这种用例，我们利用算法从嘈杂数据中综合了控制策略，从而使汽车合并到交叉点上，同时满足了汽车速度方差的其他限制
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
