Work

Policy gradient methods for discrete time linear quadratic regulator with random parameters

Year: 2024

Type: article

Abstract: This paper studies an infinite horizon optimal control problem for discrete-time linear system and quadratic criteria, both with random parameters which are independent and identically distributed with respect to time. In this general setting, we apply the policy gradient method, a reinforcement learning technique, to search for the optimal control without requiring knowledge of statistical information of the parameters. We investigate the sub-Gaussianity of the state process and establish global linear convergence guarantee for this approach based on assumptions that are weaker and easier to verify compared to existing results. Numerical experiments are presented to illustrate our result.

Source: ESAIM Control Optimisation and Calculus of Variations

DOI: 10.1051/cocv/2024014

Author: Deyue Li

Institution: Fudan University

Language: English

Topic: Adaptive Dynamic Programming Control

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid:

Funder: Fudan University