报告人：Zhuo Jin金卓（澳大利亚Macquarie大学）

邀请人：吴付科

报告时间：2023年12月23日（星期六）10:30-11:30

报告地点：科技楼南楼711

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

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. Convergence analysis of the algorithm is presented. Numerical examples are provided to illustrate the performance of the algorithm.

报告人简介：金卓教授，澳大利亚麦考瑞大学精算中心教授，2005年和2007毕业于华中科技大学数学系应用数学专业，分别获理学学士和硕士学位，2011年毕业于美国韦恩州立大学数学系数学专业，获哲学博士学位。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。研究方向为随机最优控制，随机系统的数值方法，精算学，数理金融。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。