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SUMMARY:Tutorial: Multi-Agent Reinforcement Learning - Stefano  V. Albrech
 t (DeepFlow)
DTSTART:20251106T100000Z
DTEND:20251106T130000Z
UID:TALK235624@talks.cam.ac.uk
DESCRIPTION:Multi-Agent Reinforcement Learning (MARL)\, an area of machine
  learning in which a collective of agents learn to optimally interact in a
  shared environment\, boasts a growing array of applications in modern lif
 e\, from autonomous driving and multi-robot factories to automated trading
  and energy network management. This tutorial provides an introduction to 
 the models\, solution concepts\, algorithmic ideas\, technical challenges\
 , and modern approaches in MARL. The tutorial first introduces the field's
  foundations\, including basics of reinforcement learning theory and algor
 ithms\, interactive game models\, different solution concepts for games\, 
 and the algorithmic ideas underpinning MARL research. It then details cont
 emporary MARL algorithms which leverage deep learning techniques\, coverin
 g ideas such as centralized training with decentralized execution\, value 
 decomposition\, parameter sharing\, and self-play.The tutorial follows the
  new MIT Press textbook "Multi-Agent Reinforcement Learning: Foundations a
 nd Modern Approaches"\, available at www.marl-book.com.
LOCATION:Enigma Room\, The Alan Turing Institute
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