Zobrazeno 1 - 10
of 545
pro vyhledávání: '"MULLER, Paul"'
Autor:
Li, Zun, Lanctot, Marc, McKee, Kevin R., Marris, Luke, Gemp, Ian, Hennes, Daniel, Muller, Paul, Larson, Kate, Bachrach, Yoram, Wellman, Michael P.
Multiagent reinforcement learning (MARL) has benefited significantly from population-based and game-theoretic training regimes. One approach, Policy-Space Response Oracles (PSRO), employs standard reinforcement learning to compute response policies v
Externí odkaz:
http://arxiv.org/abs/2302.00797
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
Autor:
Muller, Paul, Rowland, Mark, Elie, Romuald, Piliouras, Georgios, Perolat, Julien, Lauriere, Mathieu, Marinier, Raphael, Pietquin, Olivier, Tuyls, Karl
Recent advances in multiagent learning have seen the introduction ofa family of algorithms that revolve around the population-based trainingmethod PSRO, showing convergence to Nash, correlated and coarse corre-lated equilibria. Notably, when the numb
Externí odkaz:
http://arxiv.org/abs/2111.08350
Two-player, constant-sum games are well studied in the literature, but there has been limited progress outside of this setting. We propose Joint Policy-Space Response Oracles (JPSRO), an algorithm for training agents in n-player, general-sum extensiv
Externí odkaz:
http://arxiv.org/abs/2106.09435
Autor:
Omidshafiei, Shayegan, Hennes, Daniel, Garnelo, Marta, Tarassov, Eugene, Wang, Zhe, Elie, Romuald, Connor, Jerome T., Muller, Paul, Graham, Ian, Spearman, William, Tuyls, Karl
In multiagent environments, several decision-making individuals interact while adhering to the dynamics constraints imposed by the environment. These interactions, combined with the potential stochasticity of the agents' decision-making processes, ma
Externí odkaz:
http://arxiv.org/abs/2106.04219
Autor:
Liu, Siqi, Lever, Guy, Wang, Zhe, Merel, Josh, Eslami, S. M. Ali, Hennes, Daniel, Czarnecki, Wojciech M., Tassa, Yuval, Omidshafiei, Shayegan, Abdolmaleki, Abbas, Siegel, Noah Y., Hasenclever, Leonard, Marris, Luke, Tunyasuvunakool, Saran, Song, H. Francis, Wulfmeier, Markus, Muller, Paul, Haarnoja, Tuomas, Tracey, Brendan D., Tuyls, Karl, Graepel, Thore, Heess, Nicolas
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals de
Externí odkaz:
http://arxiv.org/abs/2105.12196