报告题目：Deep Reinforcement Learning in Insurance Risk Management
报告人：金卓教授（澳大利亚麦考瑞大学）
时间：2026年7月17日上午10:00-11:30
地点：西安交通大学创新港涵英楼5-8001会议室
报告人简介：
澳大利亚麦考瑞大学精算中心教授，新兴风险研究中心（Emerging Risks Research Centre）联合主任。2011年至2022年在澳大利亚墨尔本大学经济系精算中心工作，2022年至今当前在澳大利亚麦考瑞大学精算中心工作。研究方向为精算学、数理金融、随机最优控制、随机系统的数值方法。在国际期刊发表70余篇论文，期刊包括Insurance Mathematics and Economics,European Journal of Operational Research,SIAM Journal on Control and Optimization,Automatica,ASTIN: Bulletin,Scandinavian Actuarial Journal。
摘要：
This paper develops a hybrid deep reinforcement learning approach to manage an insurance portfolio for diffusion models. To address the model uncertainty, we adopt the recently developed modelling of exploration and exploitation strategies in a continuous-time decision-making process with reinforcement learning. We consider an insurance portfolio management problem in which an entropy-regularized reward function and corresponding relaxed stochastic controls are formulated. To obtain the optimal relaxed stochastic controls, we develop a Markov chain approximation and stochastic approximation-based iterative deep reinforcement learning algorithm where the probability distribution of the optimal stochastic controls is approximated by neural networks. In our hybrid algorithm, both Markov chain approximation and stochastic approximation are adopted in the learning processes. The idea of using the Markov chain approximation method to find initial guesses is proposed. A stochasticapproximation is adopted to estimate the parameters of neural networks. Convergence analysis of the algorithm is presented. Numerical examples are provided to illustrate the performance of the algorithm.
