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SUMMARY:Taisiia Morozova (Uppsala University)
DTSTART:20251105T121500Z
DTEND:20251105T130000Z
DTSTAMP:20260422T155411Z
UID:gbgstats/98
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/98/
 ">Multi-Agent Reinforcement Learning for Buffered Cellular Networks</a>\nb
 y Taisiia Morozova (Uppsala University) as part of Gothenburg statistics s
 eminar\n\nLecture held in MVL14.\n\nAbstract\nWe study the use of multi-ag
 ent reinforcement learning (MARL) for buffered cellular networks\, where b
 ase stations are modelled as independent agents making transmission decisi
 ons under interference and delay constraints. The network is described thr
 ough a stochastic geometry framework with Poisson-distributed base station
 s and users\, and buffers capturing traffic arrivals and service dynamics.
  To handle the interactions between agents\, we employ a mean-field approx
 imation\, so that each agent responds to an aggregate distribution of its 
 neighbours’ states and actions. The learning problem is formulated via m
 ean-field Q-learning\, where the objective is to improve network capacity 
 while controlling delays. Initial experiments show convergence of the Q-fu
 nctions for several agents\, suggesting that the approach is well-suited t
 o this setting.\n
LOCATION:https://researchseminars.org/talk/gbgstats/98/
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