Episodic Bayesian Optimal Control with Unknown Randomness Distributions
针对随机性分布未知的数据驱动随机最优控制问题，提出一种将贝叶斯学习与最优控制相结合的情节式方法，证明了收敛性与收敛速率，并开发了针对凸成本函数和线性状态动力学的有效计算方法。
Bayesian Learning for Data-Driven Stochastic Optimal Control Stochastic optimal control (SOC) provides a principled approach to dynamic decision making under uncertainty. It models the transition of the system state with a dynamic equation driven by randomness, with the assumption that the distribution of randomness is known. However, in practical problems, modeling randomness frequently depends on data or observations, which introduces uncertainty regarding the distribution of randomness. To address the data-driven SOC problem, in “Episodic Bayesian Optimal Control with Unknown Randomness Distributions,” Shapiro, Zhou, Lin, and Wang propose a new approach that incorporates Bayesian learning with optimal control in an episodic manner. They show the convergence and convergence rate results for their approach. They also develop an efficient computational method for a class of problems that have convex cost functions and linear state dynamics.