1 code implementation • 24 Jan 2024 • Ian Gemp, Yoram Bachrach, Marc Lanctot, Roma Patel, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, SiQi Liu, Karl Tuyls
A suitable model of the players, strategies, and payoffs associated with linguistic interactions (i. e., a binding to the conventional symbolic logic of game theory) would enable existing game-theoretic algorithms to provide strategic solutions in the space of language.
no code implementations • 10 Jan 2024 • SiQi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess
We then introduce NeuPL-JPSRO, a neural population learning algorithm that benefits from transfer learning of skills and converges to a Coarse Correlated Equilibrium (CCE) of the game.
1 code implementation • 5 Dec 2023 • Marc Lanctot, Kate Larson, Yoram Bachrach, Luke Marris, Zun Li, Avishkar Bhoopchand, Thomas Anthony, Brian Tanner, Anna Koop
We argue that many general evaluation problems can be viewed through the lens of voting theory.
no code implementations • 1 Feb 2023 • Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman
Multiagent reinforcement learning (MARL) has benefited significantly from population-based and game-theoretic training regimes.
no code implementations • 17 Oct 2022 • Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, SiQi Liu, Karl Tuyls
We argue that such a network is a powerful component for many possible multiagent algorithms.
no code implementations • 5 Oct 2022 • Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen Mcaleer, Jerome Connor, Karl Tuyls, Thore Graepel
Rating strategies in a game is an important area of research in game theory and artificial intelligence, and can be applied to any real-world competitive or cooperative setting.
no code implementations • 22 Sep 2022 • Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls
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 humanoids in difficult team coordination tasks.
no code implementations • 31 May 2022 • SiQi Liu, Marc Lanctot, Luke Marris, Nicolas Heess
Learning to play optimally against any mixture over a diverse set of strategies is of important practical interests in competitive games.
no code implementations • ICLR 2022 • SiQi Liu, Luke Marris, Daniel Hennes, Josh Merel, Nicolas Heess, Thore Graepel
Learning in strategy games (e. g. StarCraft, poker) requires the discovery of diverse policies.
1 code implementation • 17 Jun 2021 • Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
Two-player, constant-sum games are well studied in the literature, but there has been limited progress outside of this setting.
1 code implementation • 25 May 2021 • SiQi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan D. Tracey, Karl Tuyls, Thore Graepel, Nicolas Heess
In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.
1 code implementation • ICLR 2020 • Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Si-Qi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO).
1 code implementation • NeurIPS 2018 • Sergey Bartunov, Adam Santoro, Blake A. Richards, Luke Marris, Geoffrey E. Hinton, Timothy Lillicrap
Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance.
no code implementations • 3 Jul 2018 • Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel
Recent progress in artificial intelligence through reinforcement learning (RL) has shown great success on increasingly complex single-agent environments and two-player turn-based games.