Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Julien Perolat"'
Autor:
Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Perolat, Remi Munos
Publikováno v:
Scientific Reports, Vol 9, Iss 1, Pp 1-29 (2019)
Abstract We introduce α-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains
Externí odkaz:
https://doaj.org/article/fdd2f745b2844cf49e6facb5a043ddfa
Publikováno v:
Journal of Artificial Intelligence Research. 71:925-951
A core question in multi-agent systems is understanding the motivations for an agent's actions based on their behavior. Inverse reinforcement learning provides a framework for extracting utility functions from observed agent behavior, casting the pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6a2f38f39c0b3586957566ff7c91b72
https://doi.org/10.1613/jair.1.12594
https://doi.org/10.1613/jair.1.12594
Autor:
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, Aleksandra Malysheva, 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
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet maste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43318777c84d87510982d653d787e4b1
http://arxiv.org/abs/2206.15378
http://arxiv.org/abs/2206.15378
Autor:
Daniel Hennes, Gregory Thornton, Bart De Vylder, S. M. Ali Eslami, Trevor Back, Zhe Wang, Pauline Luc, Karl Tuyls, Alex Bridgland, Nathalie Beauguerlange, Rémi Munos, Praneet Dutta, Alexandre Galashov, Jerome T. Connor, Tim Waskett, William Spearman, Razia Ahamed, Mark Rowland, Andrew Jaegle, Dafydd Steele, Julien Perolat, Shayegan Omidshafiei, Simon Bouton, Jackson Broshear, Kris Cao, Paul Muller, Nicolas Heess, Michal Valko, Demis Hassabis, Marta Garnelo, Adrià Recasens, Ian Graham, Thore Graepel, Pablo Sprechmann, Romuald Elie, Pol Moreno
Publikováno v:
Journal of Artificial Intelligence Research. 71:41-88
The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b090b6aea89e474795d9000253494869
https://doi.org/10.1613/jair.1.12505
https://doi.org/10.1613/jair.1.12505
Autor:
Sarah Perrin, Julien Perolat, Romuald Elie, Olivier Pietquin, Mathieu Laurière, Matthieu Geist
Publikováno v:
IJCAI
IJCAI, Aug 2021, Montreal, Canada
IJCAI, Aug 2021, Montreal, Canada
International audience; We present a method enabling a large number of agents to learn how to flock, which is a natural behavior observed in large populations of animals. This problem has drawn a lot of interest but requires many structural assumptio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09daf0127bc55831a8c529f4dff81ef1
https://hal.archives-ouvertes.fr/hal-03416242
https://hal.archives-ouvertes.fr/hal-03416242
Autor:
Guy Barash, Mauricio Castillo-Effen, Niyati Chhaya, Peter Clark, Huáscar Espinoza, Eitan Farchi, Christopher Geib, Odd Erik Gundersen, Seán HÉigeartaigh, José Hernández-Orallo, Chiori Hori, Xiaowei Huang, Kokil Jaidka, Pavan Kapanipathi, Sarah Keren, Seokhwan Kim, Marc Lanctot, Danny Lange, Julian McAuley, David Martinez, Marwan Mattar, null Mausam, Martin Michalowski, Reuth Mirsky, Roozbeh Mottaghi, Joseph Osborn, Julien Perolat, Martin Schmid, Arash Shaban-Nejad, Onn Shehory, Biplav Srivastava, William Streilein, Kartik Talamadupula, Julian Togelius, Koichiro Yoshino, Quanshi Zhang, Imed Zitouni
Publikováno v:
AI Magazine. 40:67-78
The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b067cb4267e2efecb2f3f7d031938f87
https://doi.org/10.1609/aimag.v40i3.4981
https://doi.org/10.1609/aimag.v40i3.4981
Autor:
Wojciech Marian Czarnecki, Shayegan Omidshafiei, Audrunas Gruslys, Rémi Munos, Julien Perolat, Bart De Vylder, Jerome T. Connor, Mark Rowland, Paul Muller, Daniel Hennes, Francisco C. Santos, Karl Tuyls
Publikováno v:
Nature Communications
Nature Communications, Vol 11, Iss 1, Pp 1-17 (2020)
Nature Communications, Vol 11, Iss 1, Pp 1-17 (2020)
Multiplayer games have long been used as testbeds in artificial intelligence research, aptly referred to as the Drosophila of artificial intelligence. Traditionally, researchers have focused on using well-known games to build strong agents. This prog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b267c5e027b84d80d8eb3230a4c6d5e
http://arxiv.org/abs/2005.01642
http://arxiv.org/abs/2005.01642
Autor:
Richard Everett, Csaba Szepesvári, Joel Z. Leibo, Edward Hughes, Julien Perolat, Thore Graepel, Karl Tuyls, Marc Lanctot
Publikováno v:
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS
This paper provides several theoretical results for empirical game theory. Specifically, we introduce bounds for empirical game theoretical analysis of complex multi-agent interactions. In doing so we provide insights in the empirical meta game showi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d27236cd731c967a18a150c43bb3d3e3
http://livrepository.liverpool.ac.uk/3077039/1/Tuyls2019_Article_BoundsAndDynamicsForEmpiricalG.pdf
http://livrepository.liverpool.ac.uk/3077039/1/Tuyls2019_Article_BoundsAndDynamicsForEmpiricalG.pdf
Autor:
Julien Perolat, Rémi Munos, Mark Rowland, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Wojciech Marian Czarnecki, Christos H. Papadimitriou, Georgios Piliouras, Marc Lanctot, Karl Tuyls
Publikováno v:
Scientific Reports
Scientific Reports, Vol 9, Iss 1, Pp 1-29 (2019)
Scientific Reports, Vol 9, Iss 1, Pp 1-29 (2019)
We introduce α-Rank, a principled evolutionary dynamics methodology, for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamical game-theoretic solution concept called Markov-Conley chains (MCCs).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e91807ccedf76188457cc17ccacf67b
https://pubmed.ncbi.nlm.nih.gov/31289288
https://pubmed.ncbi.nlm.nih.gov/31289288
Autor:
Julien Perolat, Dustin Morrill, Finbarr Timbers, Karl Tuyls, Jean-Baptiste Lespiau, Edward Lockhart, Marc Lanctot
Publikováno v:
IJCAI
In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents. We prove that when
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24b04d01c8f8cea547412dc7adcbb49f