Browse by UCL people
Group by: Type | Date
Number of items: 28.
2021
Dafoe, A;
Bachrach, Y;
Hadfield, G;
Horvitz, E;
Larson, K;
Graepel, T;
(2021)
Cooperative AI: machines must learn to find common ground.
Nature
, 593
(7857)
pp. 33-36.
10.1038/d41586-021-01170-0.
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Marris, Luke;
Muller, Paul;
Lanctot, Marc;
Tuyls, Karl;
Graepel, Thore;
(2021)
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers.
In: Meila, M and Zhang, T, (eds.)
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 7480-7491).
Proceedings of Machine Learning Research
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Tuyls, K;
Omidshafiei, S;
Muller, P;
Wang, Z;
Connor, J;
Hennes, D;
Graham, I;
... Hassabis, D; + view all
(2021)
Game Plan: What AI can do for Football, and What Football can do for AI.
Journal of Artificial Intelligence Research
, 71
pp. 41-88.
10.1613/jair.1.12505.
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2020
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Anthony, TW;
Eccles, T;
Tacchetti, A;
Kramár, J;
Gemp, IM;
Hudson, TC;
Porcel, N;
... Bachrach, Y; + view all
(2020)
Learning to Play No-Press Diplomacy with Best Response Policy Iteration.
In:
Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020).
NeurIPS
(In press).
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Bachrach, Y;
Everett, R;
Hughes, E;
Lazaridou, A;
Leibo, JZ;
Lanctot, M;
Johanson, M;
... Graepel, T; + view all
(2020)
Negotiating team formation using deep reinforcement learning.
Artificial Intelligence
, 288
, Article 103356. 10.1016/j.artint.2020.103356.
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Balduzzi, D;
Czarnecki, WM;
Anthony, T;
Gemp, IM;
Hughes, E;
Leibo, JZ;
Piliouras, G;
(2020)
Smooth markets: A basic mechanism for organizing gradient-based learners.
In:
Proceedings of the 8th International Conference on Learning Representations, ICLR 2020.
(pp. pp. 1-18).
ICLR
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Baumann, T;
Graepel, T;
Shawe-Taylor, J;
(2020)
Adaptive Mechanism Design: Learning to Promote Cooperation.
In:
Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN).
IEEE: Glasgow, UK.
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Muller, P;
Omidshafiei, S;
Rowland, M;
Tuyls, K;
Pérolat, J;
Liu, S;
Hennes, D;
... Munos, R; + view all
(2020)
A Generalized Training Approach for Multiagent Learning.
In:
Proceedings of the 8th International Conference on Learning Representations, ICLR 2020.
(pp. pp. 1-35).
ICLR
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2019
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Balduzzi, D;
Garnelo, M;
Bachrach, Y;
Czarnecki, W;
Pérolat, J;
Jaderberg, M;
Graepel, T;
(2019)
Open-ended learning in symmetric zero-sum games.
In: Chaudhuri, K and Salakhutdinov, R, (eds.)
Proceedings of the 36th International Conference on Machine Learning.
(pp. pp. 434-443).
PMLR
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Banarse, D;
Bachrach, Y;
Liu, S;
Lever, G;
Heess, N;
Fernando, C;
Kohli, P;
(2019)
The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution.
In: Elkind, Edith and Veloso, Manuela, (eds.)
Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
(pp. pp. 1134-1142).
IFAAMAS (International Foundation of Autonomous Agents and MultiAgent Systems): Montreal, Canada.
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Eccles, T;
Bachrach, Y;
Lever, G;
Lazaridou, A;
Graepel, T;
(2019)
Biases for Emergent Communication in Multi-agent Reinforcement Learning.
In: Wallach, HM and Larochelle, H and Beygelzimer, A and d'Alché-Buc, F and Fox, EA and Garnett, R, (eds.)
Proceedings of Advances in Neural Information Processing Systems 32 (NIPS 2019).
(pp. pp. 13111-13121).
Neural Information Processing Systems Foundation, Inc.: Vancouver, Canada.
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Jaderberg, M;
Czarnecki, WM;
Dunning, I;
Marris, L;
Lever, G;
Castaneda, AG;
Beattie, C;
... Graepel, T; + view all
(2019)
Human-level performance in 3D multiplayer games with population-based reinforcement learning.
Science
, 364
(6443)
pp. 859-865.
10.1126/science.aau6249.
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Leibo, JZ;
Perolat, J;
Hughes, E;
Wheelwright, S;
Marblestone, AH;
Duenez-Guzman, E;
Sunehag, P;
... Graepel, T; + view all
(2019)
Malthusian Reinforcement Learning.
In: Elkind, Edith and Veloso, Manuela, (eds.)
Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
(pp. pp. 1099-1107).
IFAAMAS (International Foundation of Autonomous Agents and MultiAgent Systems): Montreal, Canada.
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Letcher, A;
Balduzzi, D;
Racaniere, S;
Martens, J;
Foerster, J;
Tuyls, K;
Graepel, T;
(2019)
Differentiable Game Mechanics.
Journal of Machine Learning Research
, 20
(84)
pp. 1-40.
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Sunehag, P;
Lever, G;
Liu, S;
Merel, J;
Heess, N;
Leibo, JZ;
Hughes, E;
... Graepel, T; + view all
(2019)
Reinforcement Learning Agents acquire Flocking and Symbiotic Behaviour in Simulated Ecosystems.
In: Fellermann, H and Bacardit, J and GoniMoreno, A and Fuchslin, R, (eds.)
Proceedings of the Artificial Life Conference.
(pp. pp. 103-110).
MIT Press
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Tacchetti, A;
Francis Song, H;
Mediano, PAM;
Zambaldi, V;
Kramár, J;
Rabinowitz, NC;
Graepel, T;
... Battaglia, PW; + view all
(2019)
Relational forward models for multi-agent learning.
In:
Proceedings of the 7th International Conference on Learning Representations, ICLR 2019.
ICLR
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Tuyls, K;
Pérolat, J;
Lanctot, M;
Hughes, E;
Everett, R;
Leibo, JZ;
Szepesvári, C;
(2019)
Bounds and dynamics for empirical game theoretic analysis.
Autonomous Agents and Multi-Agent Systems
, 34
, Article 7. 10.1007/s10458-019-09432-y.
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2018
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Balduzzi, D;
Racaniere, S;
Martens, J;
Foerster, J;
Tuyls, K;
Graepel, T;
(2018)
The Mechanics of n-Player Differentiable Games.
In: Dy, JG and Krause, A, (eds.)
Proceedings of the 35th International Conference on Machine Learning.
(pp. pp. 363-372).
JMLR.org
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Balduzzi, D;
Tuyls, K;
Pérolat, J;
Graepel, T;
(2018)
Re-evaluating evaluation.
In: Bengio, S and Wallach, HM and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R, (eds.)
Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018).
(pp. pp. 3272-3283).
Neural Information Processing Systems Foundation, Inc.: Montréal, Canada.
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Hughes, E;
Leibo, JZ;
Phillips, M;
Tuyls, K;
Duenez-Guzman, E;
Castaneda, AG;
Dunning, I;
... Graepel, T; + view all
(2018)
Inequity aversion improves cooperation in intertemporal social dilemmas.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 31 (NIPS 2018).
(pp. pp. 1-11).
Neural Information Processing Systems Foundation, Inc.
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Silver, D;
Hubert, T;
Schrittwieser, J;
Antonoglou, I;
Lai, M;
Guez, A;
Lanctot, M;
... Hassabis, D; + view all
(2018)
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.
Science
, 362
(6419)
pp. 1140-1144.
10.1126/science.aar6404.
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Tuyls, K;
Perolat, J;
Lanctot, M;
Leibo, JZ;
Graepel, T;
(2018)
A Generalised Method for Empirical Game Theoretic Analysis.
In:
AAMAS '18: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems.
(pp. pp. 77-85).
ACM: Stockholm, Sweden.
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Tuyls, K;
Perolat, J;
Lanctot, M;
Ostrovski, G;
Savani, R;
Leibo, JZ;
Ord, T;
... Legg, S; + view all
(2018)
Symmetric Decomposition of Asymmetric Games.
Scientific Reports
, 8
, Article 1015. 10.1038/s41598-018-19194-4.
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2017
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Lanctot, M;
Zambaldi, V;
Gruslys, A;
Lazaridou, A;
Tuyls, K;
Perolat, J;
Silver, D;
(2017)
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning.
In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.)
Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017).
Neural Information Processing Systems (NIPS): Long Beach, CA, USA.
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Leibo, JZ;
Zambaldi, VF;
Lanctot, M;
Marecki, J;
Graepel, T;
(2017)
Multi-agent Reinforcement Learning in Sequential Social Dilemmas.
In: Larson, K and Winikoff, M and Das, S and Durfee, EH, (eds.)
Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AA-MAS 2017).
(pp. pp. 464-473).
ACM: New York, USA.
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Perolat, J;
Leibo, JZ;
Zambaldi, V;
Beattie, C;
Tuyls, K;
Graepel, T;
(2017)
A multi-agent reinforcement learning model of common-pool resource appropriation.
In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.)
Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017).
Neural Information Processing Systems (NIPS)
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Silver, D;
Schrittwieser, J;
Simonyan, K;
Antonoglou, I;
Huang, A;
Guez, A;
Hubert, T;
... Hassabis, D; + view all
(2017)
Mastering the game of Go without human knowledge.
Nature
, 550
(7676)
pp. 354-359.
10.1038/nature24270.
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2010
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Graepel, T;
Candela, JQ;
Borchert, T;
Herbrich, R;
(2010)
Web-Scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft's Bing search engine.
In: Fürnkranz, J and Joachims, T, (eds.)
Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel.
(pp. 13 - 20).
Omnipress
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