数学科学学院学术报告[2023]105号

(高水平大学建设系列报告877号)

报告题目: A Hybrid Deep Reinforcement Learning Method for Insurance Portfolio Management

报告人：金卓教授（澳大利亚麦考瑞大学）

报告时间：2023.12.20 15:00-16:00pm

讲座地点：汇星楼（科技楼）514

报告内容：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. Convergence analysis of the algorithm is presented. Numerical examples are provided to illustrate the performance of the algorithm.

报告人简历：金卓，博士，澳大利亚麦考瑞大学教授，精算和商业分析系的研究主任，北美准精算师（ASA）。历任澳大利亚墨尔本大学精算中心讲师，高级讲师，副教授。研究方向为随机最优控制，随机系统的数值方法，精算学，数理金融，机器学习。在国际顶级期刊发表70余篇论文，期刊包括SIAM Journal on Control and Optimization, European Journal of Operational Research, Insurance Mathematics and Economics, Automatica, ASTIN: Bulletin and Scandinavian Actuarial Journal，Journal of Risk and Insurance等。

欢迎师生参加！

邀请人：李婧超

数学科学学院

2023年12月18日