• SDS Portal
Search
CUHK-Shenzhen
简体中文
  • About SDS
    • Overview
    • Academic Area
    • Dean’s Message
    • Publications
      • Brochure
      • School Newsletter
      • Annual Report
    • FAQ
    • Contact Us
  • Programmes
    • Introduction
    • Undergraduate
      • Data Science and Big Data Technology
      • Statistics
      • Computer Science and Engineering
      • Financial Engineering
      • 2+2 Double Major Programme
        • Interdisciplinary Data Analytics + X Double Major Programme
        • Aerospace Science and Earth Informatics + X Double Major Programme
    • Taught Postgraduate
      • M.Sc in Data Science
      • M.Sc in Financial Engineering(Full-time/Part-time)
      • M.Sc in Artificial Intelligence and Robotics
      • M.Sc in Computer Science
      • M.Sc in Statistics
      • M.Sc in Bioinformatics
    • Research Postgraduate
      • M.Phil.-Ph.D. Programme in Data Science
      • M.Phil.-Ph.D. Programme in Computer Science
  • Faculty
    • Faculty
    • Emeritus Faculty
    • Affiliated Appointments
    • Researchers/Visitors
  • Students
    • Ph.D. Students
    • Student Interviews
  • News & Announcements
    • News
    • Announcements
  • School Events
    • Academic Conferences
      • ICSR+InnoBiz 2024
      • CSAMSE 2023
      • RMTA 2023
      • ICASSP 2022
      • Mostly OM 2019
    • Academic Activities
    • SDS Colloquium Series
    • Other Events
  • Research
  • Jobs
    • Faculty Positions
    • Postdoctoral Fellowships
  • Career
    • Graduate Placements
    • International Programmes
  • About SDS
    • Overview
    • Academic Area
    • Dean’s Message
    • Publications
      • Brochure
      • School Newsletter
      • Annual Report
    • FAQ
    • Contact Us
  • Programmes
    • Introduction
    • Undergraduate
      • Data Science and Big Data Technology
      • Statistics
      • Computer Science and Engineering
      • Financial Engineering
      • 2+2 Double Major Programme
        • Interdisciplinary Data Analytics + X Double Major Programme
        • Aerospace Science and Earth Informatics + X Double Major Programme
    • Taught Postgraduate
      • M.Sc in Data Science
      • M.Sc in Financial Engineering(Full-time/Part-time)
      • M.Sc in Artificial Intelligence and Robotics
      • M.Sc in Computer Science
      • M.Sc in Statistics
      • M.Sc in Bioinformatics
    • Research Postgraduate
      • M.Phil.-Ph.D. Programme in Data Science
      • M.Phil.-Ph.D. Programme in Computer Science
  • Faculty
    • Faculty
    • Emeritus Faculty
    • Affiliated Appointments
    • Researchers/Visitors
  • Students
    • Ph.D. Students
    • Student Interviews
  • News & Announcements
    • News
    • Announcements
  • School Events
    • Academic Conferences
      • ICSR+InnoBiz 2024
      • CSAMSE 2023
      • RMTA 2023
      • ICASSP 2022
      • Mostly OM 2019
    • Academic Activities
    • SDS Colloquium Series
    • Other Events
  • Research
  • Jobs
    • Faculty Positions
    • Postdoctoral Fellowships
  • Career
    • Graduate Placements
    • International Programmes
  • SDS Portal
CUHK-Shenzhen
简体中文

Breadcrumb

  • Home
  • School Events
  • SDS Colloquium Series
  • 【SDS Colloquium Series】On System Theory for Learning in Games

【SDS Colloquium Series】On System Theory for Learning in Games

March 24, 2025 SDS Colloquium Series

SDS Colloquium Series

TopicOn System Theory for Learning in Games
SpeakerLacra PAVEL, Professor, Department of Electrical and Computer Engineering, University of Toronto
Host

Shi PU, Assistant Professor, School of Data Science, CUHK-Shenzhen

Junfeng WU, Associate Professor, School of Data Science, CUHK-Shenzhen

Date24 March (Monday), 2025
Time11:00 AM - 12:00 PM, Beijing Time
FormatOnsite
Venue103 Meeting Room, Daoyuan Building
LanguageEnglish

Abstract

In this talk we focus on the role played by system theory in analysis and design of learning algorithms in games. Over the years, a plethora of algorithms/dynamics have been proposed: from best-response play, (projected) gradient-play and proximal dynamics to fictitious-play, payoff-based play or Q-learning (reinforcement-learning), the list is long. Why is it that in certain game settings some algorithms work while others don’t? How can we relax their assumptions and how can we generalize them in a systematic manner?

This is a topic of increased interest in recent years. In this talk we review some of our group’s contributions towards answering them. Our approach is based on exploiting system theory principles and connections to passivity/dissipativity. We show that some popular game-theoretic algorithms can be cast as instances of a feedback interconnection between a dissipative/passive dynamical system and some game mapping. Once this is done, convergence analysis of learning dynamics follows from standard passivity theory, based on simple and concise arguments. We also discuss how passivity-inspired ideas can be used to design novel algorithms and learning dynamics, for both Nash and generalized Nash equilibrium problems. We follow with discussion of higher-order learning dynamics designed based on passivity. We close with extensions to learning for agents with intrinsic dynamics.

Biography

Lacra Pavel is a Professor in the Systems Control group, Department of Electrical and Computer Engineering, University of Toronto, Canada. She received the Diploma of Engineer in Automatic Control from Technical University Iasi, Romania and the Ph.D. degree in Electrical Engineering from Queen’s University, Canada. She joined University of Toronto in 2002, after a postdoctoral stage at the National Research Council and four years of working in the communication industry. Her research interests are in game theory and distributed optimization in networks, with emphasis on dynamics and control methods. She is the author of the book Game Theory for Control of Optical Networks (Birkhäuser-Springer Science). She is an IEEE Fellow. She is acting as the Editor-in-Chief for the IEEE Transactions on Control of Network Systems and as an Associate Editor for the IEEE Transactions on Automatic Control.
Address: 3 - 6 Floor, Dao Yuan Building, 2001 Longxiang Road, Longgang District, Shenzhen
E-mail: sds@cuhk.edu.cn
Wechat Account: cuhksz-sds

sds.cuhk.edu.cn

Copyright © CUHK-Shenzhen School of Data Science