Programme Committee

  • Thomas Anthony
  • Mohammad Azar
  • Yoram Bachrach
  • Nolan Bard
  • Noam Brown
  • Michael Buro
  • Tristan Cazenave
  • Elnaz Davoodi
  • Ryan D'Orazio
  • Romuald Elie
  • Gabriele Farina
  • Chao Gao
  • Ian Gemp
  • Audrunas Grusyls
  • Daniel Guo
  • Daniel Hennes
  • Thomas Hubert
  • Rudolf Kadlec
  • Bilal Kartal
  • Guy Lever
  • Edward Lockhart
  • Diego Pérez Liébana
  • Viliam Lisy
  • Simon Lucas
  • Stephen McAleer
  • Matej Moravcik
  • Dustin Morrill
  • Shayegan Omidshafiei
  • Georgios Piliouras
  • Santiago Ontanon
  • Laurent Orseau
  • Olivier Pietquin
  • Bilal Piot
  • Mark Rowland
  • Julian Schrittwieser
  • John Schultz
  • Samuel Sokota
  • Michal Sustr
  • Finbarr Timbers
  • Karl Tuyls
  • Kevin Waugh
  • James Wright


The organizing committee consists of expertise in games, multiagent reinforcement learning and planning, computational game theory, and machine learning.

Martin Schmid is a research scientist at DeepMind. He is the a co-author of DeepStack, the first expert no-limit Poker AI, which brings ideas of local search and value functions from RL to imperfect information games. Before joining DeepMind, he worked as a research scientist for IBM Watson.

Marc Lanctot is a research scientist at DeepMind focused on general multiagent reinforcement learning. Marc obtained his PhD in 2013 from University of Alberta, where he worked on sampling methods for regret minimzation in games. Before joining DeepMind, he worked as a post-doctoral fellow at Maastricht University on search in games.

Julien Pérolat is a research scientist at DeepMind, and has worked on reinforcement learning in Markov games. He obtained his PhD in 2017 at University of Lille. He has co-organized a Multiagent (Deep) Learning tutorial at AAMAS 2018.