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SUMMARY:Learning equilibria in games with bandit feedback - Maryam Kamgarp
 our (EPFL - Ecole Polytechnique Fédérale de Lausanne)
DTSTART:20251112T090000Z
DTEND:20251112T094000Z
UID:TALK238483@talks.cam.ac.uk
DESCRIPTION:A central challenge in large-scale engineering systems\, such 
 as energy and transportation networks\, is enabling autonomous decision-ma
 king among interacting agents. Game theory provides a natural framework to
  model and analyze such problems. In practice\, however\, agents often hav
 e only partial information about the costs and actions of others. This mak
 es decentralized learning a key tool for developing effective strategies. 
 In this talk\, I will discuss recent advances in decentralized learning fo
 r static and Markov games under bandit feedback. I will outline algorithms
  with convergence guarantees and highlight directions for future research.
LOCATION:Seminar Room 1\, Newton Institute
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