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The Algorithmic Learning in Games seminar is a joint effort between faculty at London School of Economics, the University of Waterloo, Imperial College London and, King's College London.
We meet once every fortnight to discuss problems and research in a range of topics related to:
Game Theory
Multi-Agent Reinforcement Learning
Learning in Games
Multi-Agent Systems
Recordings of our past sessions can be found here.
Next Talk
Our next talk will be lead by Martin Bichler on December 8th 2025 at 16:00 GMT. Please see further details below:
The Convergence of Learning Algorithms in Auction Games
The Convergence of Learning Algorithms in Auction Games
Martin Bichler -Â Professor of Computer Science, Technical University of Munich (TUM)
Many digital markets, such as display advertising exchanges, are run as repeated first- or second-price auctions and are increasingly automated by learning agents. Recent empirical work shows that simple learning algorithms converge to an equilibrium in such settings, yet the reasons for this convergence remain elusive. We model the equilibrium problem as an infinite-dimensional variational inequality and analyze the associated dynamical system induced by gradient-based learning. We show that known sufficient conditions for convergence do not hold, but are able to prove asyptotic stability of the equilibrium. Our approach establishes a new framework for analyzing the convergence of learning dynamics in these games.
Organisers
London School of Economics
University of Waterloo
Imperial College London
King's College London
Schedule
We intend to meet every other Monday from 16:00 - 17:00 GMT - please allow for slight changes to this schedule as needed.
Upcoming Speakers
Past Speakers
Recordings of past sessions can be found here or via the title of the respective talk in the spreadsheet below.
Sign-Up Form
We are open to any interested in joining the seminar, both as a member of the audience and as a speaker. To get in touch, please fill out the form below and we will get back to you as soon as possible.
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