数学科学学院学术报告[2026]004号

(高水平大学建设系列报告1263号)

报告题目：Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions

报告人：刘杨 助理教授【香港中文大学（深圳）】

报告时间：2026年1月13日 14:15-15:15

报告地点：汇文楼2331

报告内容：We propose a reinforcement learning (RL) framework under a broad class of risk objectives, characterized by convex scoring functions. This class covers many common risk measures, such as variance, Expected Shortfall, entropic Value-at-Risk, and mean-risk utility. To resolve the time-inconsistency issue, we consider an augmented state space and an auxiliary variable and recast the problem as a two-state optimization problem. We propose a customized Actor-Critic algorithm and establish some theoretical approximation guarantees. A key theoretical contribution is that our results do not require the Markov decision process to be continuous. Additionally, we propose an auxiliary variable sampling method inspired by the alternating minimization algorithm, which is convergent under certain conditions. We validate our approach in simulation experiments with a financial application in statistical arbitrage trading, demonstrating the effectiveness of the algorithm. This joint work is with Shanyu Han and Xiang Yu.

报告人简历：刘杨博士目前担任香港中文大学（深圳）理工学院助理教授。他分别于2016年和2021年获得清华大学数学学士和博士学位，之后在滑铁卢大学和斯坦福大学担任博士后研究员。他的研究领域包括金融数学、应用概率、运筹学、精算学、强化学习。刘杨博士致力于研究金融、保险和强化学习中的随机复杂系统与结构和不确定性下的决策，具体包括非凹/凸效用投资组合优化、相依不确定下的稳健风险聚合与风险度量等问题。研究成果发表于领域内著名学术期刊，如Operations Research、Mathematical Finance、Finance and Stochastics、SIAM Journal on Control and Optimization、Insurance: Mathematics and Economics等。他于2024年获得中国运筹学会金融工程与金融风险管理分会第十三届学术年会青年学者最佳论文一等奖。            

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邀请人：董海玲

数学科学学院

2026年1月8日