时 间：2025年10月27 日（周一）13：30 – 14：30
地 点：普陀校区理科大楼A1514室
报告人：David Landriault 滑铁卢大学教授
主持人：李丹萍 华东师范大学教授
摘 要：
In this talk, a Reinforcement Learning (RL) approach is considered to examine the optimal reinsurance problem when the insurer faces uncertainty about the claim frequency or severity distributions. To this end, we first formulate an exploratory version of the problem as a relaxed stochastic control problem. Within a broad class of parametric retention functions and general risk loading functions, we derive the closed-form optimal policy under the continuous-time mean–variance criterion. This is achieved through a formal verification theorem and solving classical solutions of a system of exploratory extended Hamilton-Jacobi-Bellman (EEHJB) equations. We then establish a policy iteration theorem, showing that starting from any time- and state-homogeneous policy, policy iteration converges to the derived optimal policy. Next, we develop a martingale orthogonality theorem, which serves as the foundation of our RL algorithm. Finally, we demonstrate the convergence of the algorithm through numerical studies.
报告人简介：
David Landriault is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Professor Landriault held a Tier-II Canada Research Chair in Insurance Risk Processes from 2011 to 2021. He also served as the Associate Chair for Actuarial Science within the Department for a period of 6 years. Professor Landriault is an Associate Editor of the top-tier actuarial journal, Insurance: Mathematics and Economics. He is also a Fellow of both the Canadian Institute of Actuaries and the Society of Actuaries. Professor Landriault’s research interest relates to the applications of quantitative risk management techniques to assess and actively manage a variety of insurance and financial risks. Most specifically, Professor Landriault’s areas of research expertise include ruin theory, insurance risk processes, stochastic control problems in insurance and finance and variable annuities.