报告题目：Stochastic Linear Quadratic Optimal Control Problem: A Reinforcement Learning Method

报告人：李迅，香港理工大学应用数学系

报告时间：2022年6月20日 15:00-16:00 (北京时间)

报告地点：腾讯会议 ID 175-964-816

主持人：吴先萍

报告摘要：This talk adopts a reinforcement learning (RL) method to solve infinite horizon continuous-time stochastic linear quadratic problems, where the drift and diffusion terms in the dynamics may depend on both the state and control. Based on Bellman’s dynamic programming principle, we present an online RL algorithm to attain optimal control with partial system information. This algorithm computes the optimal control rather than estimates the system coefficients and solves the related Riccati equation. It only requires local trajectory information, which significantly simplifies the calculation process. We shed light on our theoretical findings using two numerical examples.

简介：李迅，香港理工大学教授，博导，主要研究领域为随机控制和金融应用。在《SIAM Journal on Control and Optimization》、《Annals of Applied Probability》、《Journal of Differential Equations》、《IEEE Transactions on Automatic Control》、 《Automatica》、 《Mathematical Finance》等国际期刊上发表多篇论文。