报告题目： Deep Reinforcement Learning for Insurance Decision-Making

报 告 人：金卓 教授 （麦考瑞大学）

报告时间：2026年11月9日（星期一） 10:00-11:00

报告地点：数学科学学院114（小报告厅）      

校内联系人： 廖华夫 教授             联系方式：84708351-8614

报告摘要：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 stochastic approximation is adopted to estimate the parameters of neural networks.

报告人简介：金卓教授现为澳大利亚麦考瑞大学教授，精算和商业分析系研究主任，麦考瑞大学新兴风险研究中心（Emerging Risks Research Centre）联合主任，北美准精算师（ASA）。他的研究方向包括精算学、数理金融、随机最优控制、随机系统的数值方法等，他已在国际期刊发表80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》。金卓教授还担任World Scientific出版的《Advances in Statistics, Probability, and Actuarial Science》系列丛书主编之一。