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of 863
pro vyhledávání: '"Anthony, Thomas"'
Autor:
Lanctot, Marc, Larson, Kate, Bachrach, Yoram, Marris, Luke, Li, Zun, Bhoopchand, Avishkar, Anthony, Thomas, Tanner, Brian, Koop, Anna
We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings or pairwise comparisons of agents to produce an overall evaluation. By
Externí odkaz:
http://arxiv.org/abs/2312.03121
Autor:
Madhushani, Udari, McKee, Kevin R., Agapiou, John P., Leibo, Joel Z., Everett, Richard, Anthony, Thomas, Hughes, Edward, Tuyls, Karl, Duéñez-Guzmán, Edgar A.
In social psychology, Social Value Orientation (SVO) describes an individual's propensity to allocate resources between themself and others. In reinforcement learning, SVO has been instantiated as an intrinsic motivation that remaps an agent's reward
Externí odkaz:
http://arxiv.org/abs/2305.00768
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
Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse Correlated Equilibria are useful components for many multiagent machine learning algorithms. Unfortunately, solving a normal-form game could take prohibitive or non-determin
Externí odkaz:
http://arxiv.org/abs/2210.09257
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
Autor:
McManus, Benjamin, Mrug, Sylvie, Wagner, William P., Underhill, Andrea, Pawar, Piyush, Anthony, Thomas, Stavrinos, Despina
Publikováno v:
In Transportation Research Part F: Psychology and Behaviour August 2024 105:257-266
Publikováno v:
ICLR 2021
Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in multiagent systems by interleaving empirical game analysis with deep reinforcement learning (Deep RL). At each iteration, Deep RL is invoked to train a b
Externí odkaz:
http://arxiv.org/abs/2106.01901
Autor:
Gemp, Ian, Savani, Rahul, Lanctot, Marc, Bachrach, Yoram, Anthony, Thomas, Everett, Richard, Tacchetti, Andrea, Eccles, Tom, Kramár, János
Nash equilibrium is a central concept in game theory. Several Nash solvers exist, yet none scale to normal-form games with many actions and many players, especially those with payoff tensors too big to be stored in memory. In this work, we propose an
Externí odkaz:
http://arxiv.org/abs/2106.01285
Autor:
Hamrick, Jessica B., Friesen, Abram L., Behbahani, Feryal, Guez, Arthur, Viola, Fabio, Witherspoon, Sims, Anthony, Thomas, Buesing, Lars, Veličković, Petar, Weber, Théophane
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hyp
Externí odkaz:
http://arxiv.org/abs/2011.04021