报告摘要：
This talk is to adopt 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 the Bellman’s dynamic programming principle, we presented 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.
专家简介：
李娜，山东财经大学统计与数学学院副院长，二级教授，博士生导师，青年长江学者，首届山东省科学技术青年奖获得者。在控制论领域国际三大顶级期刊《SIAM Journal on Control and Optimization》、《Automatica》、《IEEE Transactions on Automatic Control》等国际著名学术期刊发表高水平论文20余篇；先后主持国家自然科学基金项目3项、山东省自然科学基金项目2项、山东省高等学校科技项目2项。
