Zobrazeno 1 - 10
of 177
pro vyhledávání: '"Elie, Romuald"'
Autor:
Koster, Raphael, Pîslar, Miruna, Tacchetti, Andrea, Balaguer, Jan, Liu, Leqi, Elie, Romuald, Hauser, Oliver P., Tuyls, Karl, Botvinick, Matt, Summerfield, Christopher
A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciproc
Externí odkaz:
http://arxiv.org/abs/2404.15059
Autor:
Gemp, Ian, Lanctot, Marc, Marris, Luke, Mao, Yiran, Duéñez-Guzmán, Edgar, Perrin, Sarah, Gyorgy, Andras, Elie, Romuald, Piliouras, Georgios, Kaisers, Michael, Hennes, Daniel, Bullard, Kalesha, Larson, Kate, Bachrach, Yoram
The core is a central solution concept in cooperative game theory, defined as the set of feasible allocations or payments such that no subset of agents has incentive to break away and form their own subgroup or coalition. However, it has long been kn
Externí odkaz:
http://arxiv.org/abs/2402.03928
Autor:
Wang, Zhe, Veličković, Petar, Hennes, Daniel, Tomašev, Nenad, Prince, Laurel, Kaisers, Michael, Bachrach, Yoram, Elie, Romuald, Wenliang, Li Kevin, Piccinini, Federico, Spearman, William, Graham, Ian, Connor, Jerome, Yang, Yi, Recasens, Adrià, Khan, Mina, Beauguerlange, Nathalie, Sprechmann, Pablo, Moreno, Pol, Heess, Nicolas, Bowling, Michael, Hassabis, Demis, Tuyls, Karl
Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains an open research challenge. To address this unmet need, we propose Tac
Externí odkaz:
http://arxiv.org/abs/2310.10553
Autor:
Goktas, Denizalp, Parkes, David C., Gemp, Ian, Marris, Luke, Piliouras, Georgios, Elie, Romuald, Lever, Guy, Tacchetti, Andrea
We introduce the use of generative adversarial learning to compute equilibria in general game-theoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-games, and its specific instantiation as the competitive equilibrium (CE)
Externí odkaz:
http://arxiv.org/abs/2302.06607
Autor:
Gemp, Ian, Anthony, Thomas, Bachrach, Yoram, Bhoopchand, Avishkar, Bullard, Kalesha, Connor, Jerome, Dasagi, Vibhavari, De Vylder, Bart, Duenez-Guzman, Edgar, Elie, Romuald, Everett, Richard, Hennes, Daniel, Hughes, Edward, Khan, Mina, Lanctot, Marc, Larson, Kate, Lever, Guy, Liu, Siqi, Marris, Luke, McKee, Kevin R., Muller, Paul, Perolat, Julien, Strub, Florian, Tacchetti, Andrea, Tarassov, Eugene, Wang, Zhe, Tuyls, Karl
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d hu
Externí odkaz:
http://arxiv.org/abs/2209.10958
Autor:
Muller, Paul, Elie, Romuald, Rowland, Mark, Lauriere, Mathieu, Perolat, Julien, Perrin, Sarah, Geist, Matthieu, Piliouras, Georgios, Pietquin, Olivier, Tuyls, Karl
The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts. However, as the size of an $N$-player game typically grows exponentially with $N$, standard game theoretic a
Externí odkaz:
http://arxiv.org/abs/2208.10138
Autor:
Perolat, Julien, de Vylder, Bart, Hennes, Daniel, Tarassov, Eugene, Strub, Florian, de Boer, Vincent, Muller, Paul, Connor, Jerome T., Burch, Neil, Anthony, Thomas, McAleer, Stephen, Elie, Romuald, Cen, Sarah H., Wang, Zhe, Gruslys, Audrunas, Malysheva, Aleksandra, Khan, Mina, Ozair, Sherjil, Timbers, Finbarr, Pohlen, Toby, Eccles, Tom, Rowland, Mark, Lanctot, Marc, Lespiau, Jean-Baptiste, Piot, Bilal, Omidshafiei, Shayegan, Lockhart, Edward, Sifre, Laurent, Beauguerlange, Nathalie, Munos, Remi, Silver, David, Singh, Satinder, Hassabis, Demis, Tuyls, Karl
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:
http://arxiv.org/abs/2206.15378
Autor:
Laurière, Mathieu, Perrin, Sarah, Pérolat, Julien, Girgin, Sertan, Muller, Paul, Élie, Romuald, Geist, Matthieu, Pietquin, Olivier
Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases. Introduced by Lasry and Lions, and Huang, Caines and Malham\'e, Mean Field Games (
Externí odkaz:
http://arxiv.org/abs/2205.12944
Autor:
Laurière, Mathieu, Perrin, Sarah, Girgin, Sertan, Muller, Paul, Jain, Ayush, Cabannes, Theophile, Piliouras, Georgios, Pérolat, Julien, Élie, Romuald, Pietquin, Olivier, Geist, Matthieu
Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs has gained momentum, particularly using model-free reinforcement lear
Externí odkaz:
http://arxiv.org/abs/2203.11973
This paper addresses significant obstacles that arise from the widespread use of machine learning models in the insurance industry, with a specific focus on promoting fairness. The initial challenge lies in effectively leveraging unlabeled data in in
Externí odkaz:
http://arxiv.org/abs/2112.09466