JEmpirical Dynamic Programming for Controlled Diffusion Processes

被引：0
|
作者：
Karumanchi, Sambhu H. [1 ]
Belabbas, Mohamed A. [2 ]
Hovakimyan, Naira [3 ]
机构：
[1] Univ Illinois, Dept Aerosp Engn, Urbana, IL 61801 USA
[2] Univ Illinois, Dept Elect & Comp Engn, Urbana, IL USA
[3] Univ Illinois, Dept Mech Sci & Engn, Urbana, IL USA
来源：
IFAC PAPERSONLINE | 2023年 / 56卷 / 02期
基金：
美国国家科学基金会;
关键词：
Stochastic optimal control; Numerical methods for optimal control; Diffusion; processes; Reinforcement learning; Markov decision process; Value iteration; ALGORITHM;
D O I：
10.1016/j.ifacol.2023.10.854
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider Markov chain approximation for optimal control of diffusion processes under infinite horizon discounted cost optimality and apply the simulation-based Empirical Value Iteration to estimate the value function of each approximating chain. We follow a nested multi-grid discretization of the state space to establish weak convergence of the value function sequence to the value function of the original controlled diffusion. We illustrate the convergence performance of the model on the popular Benes' bang-bang control problem [Bene.s (1974)].
引用
收藏
页码：11235 / 11241
页数：7
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