報告題目：A Hybrid Deep Reinforcement Learning Method for Insurance Portfolio Management
報告摘要：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.
報 告 人：金 卓 澳大利亞麥考瑞大學
報告時間：2024年1月10日15:30
報告地點：圖書館1612會議室
報告人簡介：
金卓,，澳大利亞麥考瑞大學精算中心教授,，2005年和2007畢業(yè)于華中科技大學數(shù)學系應用數(shù)學專業(yè),，分別獲理學學士和碩士學位,，2011年畢業(yè)于美國韋恩州立大學數(shù)學系數(shù)學專業(yè),，獲哲學博士學位。2011年至2022年在澳大利亞墨爾本大學經(jīng)濟系精算中心工作,，2022年至今當前在澳大利亞麥考瑞大學精算中心工作,。研究方向為隨機最優(yōu)控制，隨機系統(tǒng)的數(shù)值方法,，精算學,，數(shù)理金融。在國際期刊發(fā)表70余篇論文,，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,。