Topic: Game Theory

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1. Mastering the game of Stratego with model-free multiagent reinforcement learning

   Paper • Dec 1, 2022 • Science • Julien Perolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksander Malyshev, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Remi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

   Imperfect information games, where players have hidden information, represent a significant challenge for artificial intelligence. Stratego is a complex, imperfect-information board game with an en...

2. Mastering the game of Go with deep neural networks and tree search

   Paper • Jan 27, 2016 • Nature • David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Demis Hassabis

   The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and move...

3. Grandmaster level in StarCraft II using multi-agent reinforcement learning

   Paper • Oct 30, 2019 • Nature • Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, David Silver

   The game of StarCraft II has emerged as a grand challenge for artificial intelligence research owing to its complex, multi-agent, and partially observable environment. Here we introduce AlphaStar, ...