5 papers · 1 filter
Model-free stochastic linear quadratic control for discrete-time systems with multiplicative and additive noises via semidefinite programming
Jing Guo, Xiushan Jiang, Weihai Zhang
This paper investigates a model-free solution to the stochastic linear quadratic regulation (LQR) problem for linear discrete-time systems with both multiplicative and additive noi…
Primal-dual policy learning for mean-field stochastic LQR problem
Xiushan Jiang, Dong Wang, Weihai Zhang +2
Integrating data-driven techniques with mechanism-driven insights has recently gained popularity as a powerful learning approach to solving traditional LQR problems for designing i…
Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noise
Xiushan Jiang, Weihai Zhang
Reinforcement learning (RL) is an effective approach for solving optimal control problems without knowing the exact information of the system model. However, the classical Q-learni…
Model-free stochastic linear quadratic design by semidefinite programming
Jing Guo, Xiushan Jiang, Weihai Zhang
In this article, we study a model-free design approach for stochastic linear quadratic (SLQ) controllers. Based on the convexity of the SLQ dual problem and the Karush-Kuhn-Tucker…
Model-free Reinforcement Learning for Control of Stochastic Discrete-time Systems
Xiushan Jiang, Li Wang, Dongya Zhao +1
This paper proposes a reinforcement learning (RL) algorithm for infinite horizon problem in a class of stochastic discrete-time systems, rather than using…