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金融工程研究中心学术报告: Reinforcement learning for mean-field control problems
- 来源：
- 学校官网
- 收录时间：
- 2026-03-17 19:20:41
- 时间：
- 2025-12-08 10:00:00
- 地点：
- 腾讯会议 803-340-191
- 报告人：
- 魏晓利
- 学校：
- 苏州大学
- 关键词：
- reinforcement learning, mean-field control, Q-learning, q-learning, entropy regularization, stochastic control
- 简介：
- In this talk, we present a series of results on reinforcement learning (RL) for mean-field control (MFC) problems. We begin with the study of discrete-time Q-learning algorithms for MFC. We then investigate q-learning, the recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023) in the framework of entropy-regularized reinforcement learning. Our work addresses three fundamental questions in RL for MFC problems: (1) how to define an appropriate form of the Q-function (or q-function), (2) how to derive optimal policies from the Q-function (or q-function), and (3) how to learn the Q-function (or q-function) itself.
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报告介绍：
In this talk, we present a series of results on reinforcement learning (RL) for mean-field control (MFC) problems. We begin with the study of discrete-time Q-learning algorithms for MFC. We then investigate q-learning, the recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023) in the framework of entropy-regularized reinforcement learning. Our work addresses three fundamental questions in RL for MFC problems: (1) how to define an appropriate form of the Q-function (or q-function), (2) how to derive optimal policies from the Q-function (or q-function), and (3) how to learn the Q-function (or q-function) itself.
报告人介绍：
魏晓利，哈尔滨工业大学准聘副教授。本科毕业于中国科学技术大学，2018年于巴黎第七大学获得博士学位。2019-2021年在加州大学伯克利分校从事博士后。2021年-2023年在清华大学深圳国际研究生院担任助理教授。主要从事随机微分博弈、强化学习等研究。论文发表在Operations Research，Mathematical Finance, SIAM Journal on Control and Optimization等期刊杂志。
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