Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Hennes, Daniel"'
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:
Lanctot, Marc, Schultz, John, Burch, Neil, Smith, Max Olan, Hennes, Daniel, Anthony, Thomas, Perolat, Julien
Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy. In sequential decision-making, agent evaluation has largely been rest
Externí odkaz:
http://arxiv.org/abs/2303.03196
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:
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
Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, growing a policy population that is robust to exploit. This iterative ap
Externí odkaz:
http://arxiv.org/abs/2202.07415
Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving. In these settings, ML algorithms typically need to reason about a physical system using high dimensional observations,
Externí odkaz:
http://arxiv.org/abs/2111.05458
Autor:
Piliouras, Georgios, Rowland, Mark, Omidshafiei, Shayegan, Elie, Romuald, Hennes, Daniel, Connor, Jerome, Tuyls, Karl
Regret has been established as a foundational concept in online learning, and likewise has important applications in the analysis of learning dynamics in games. Regret quantifies the difference between a learner's performance against a baseline in hi
Externí odkaz:
http://arxiv.org/abs/2106.14668
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