报告人：金卓(澳大利亚麦考瑞大学)

时间：2024年06月18日 10：00-

地址: 数统学院LD402

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.

简介：金卓，博士，澳大利亚麦考瑞大学精算与商业分析系教授，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经济系精算中心讲师，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。在国际期刊发表70余篇论文，期刊包括 Insurance Mathematics and Economics, European Journal of Operational Research, Journal of Risk and Insurance, SIAM Journal on Control and Optimization, Automatica, ASTIN: Bulletin and Scandinavian Actuarial Journal.

邀请人：张志民

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