通过近近贝耶斯输入推断器进行斯托克控制 (Stochastic Control through Approximate Bayesian Input Inference)

from arxiv, Submitted to Transactions on Automatic Control Special Issue: Learning and Control. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Optimal control under uncertainty is a prevailing challenge in control, due to the difficulty in producing tractable solutions for the stochastic optimization problem. By framing the control problem as one of input estimation, advanced approximate inference techniques can be used to handle the statistical approximations in a principled and practical manner. Analyzing the Gaussian setting, we present a solver capable of several stochastic control methods, and was found to be superior to popular baselines on nonlinear simulated tasks. We draw connections that relate this inference formulation to previous approaches for stochastic optimal control, and outline several advantages that this inference view brings due to its statistical nature.

翻译：不确定性下的优化控制是控制方面的一项普遍挑战,因为很难为随机优化问题提出可移植的解决办法。通过将控制问题设定为投入估计问题,可以使用先进的近似推论技术以原则性和实用的方式处理统计近似值。分析高斯环境,我们提出了一个能够采用几种随机控制方法的溶剂,并被发现在非线性模拟任务上优于流行基线。我们绘制了与先前的随机最佳控制方法相联系的连接,并概述了这种推断观点因其统计性质而带来的若干好处。