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Thomas W. Anthony 0001
Person information
- affiliation: DeepMind, UK
Other persons with the same name
- Tom Anthony — disambiguation page
- Thomas Anthony 0002 — University of Alabama at Birmingham, AL, USA
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2020 – today
- 2023
- [j4]Max Olan Smith, Thomas W. Anthony, Michael P. Wellman
:
Learning to play against any mixture of opponents. Frontiers Artif. Intell. 6 (2023) - [j3]Max Olan Smith, Thomas W. Anthony, Michael P. Wellman:
Strategic Knowledge Transfer. J. Mach. Learn. Res. 24: 233:1-233:96 (2023) - [j2]Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Pérolat:
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [i17]Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas W. Anthony, Julien Pérolat:
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. CoRR abs/2303.03196 (2023) - [i16]Udari Madhushani, Kevin R. McKee, John P. Agapiou, Joel Z. Leibo, Richard Everett, Thomas W. Anthony, Edward Hughes, Karl Tuyls, Edgar A. Duéñez-Guzmán
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas. CoRR abs/2305.00768 (2023) - [i15]Marc Lanctot, Kate Larson
Evaluating Agents using Social Choice Theory. CoRR abs/2312.03121 (2023) - 2022
- [j1]Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán
Developing, evaluating and scaling learning agents in multi-agent environments. AI Commun. 35(4): 271-284 (2022) - [c9]Ian Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár:
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. AAMAS 2022: 507-515 - [c8]Luke Marris, Ian Gemp, Thomas Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls:
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. NeurIPS 2022 - [d1]Julien Pérolat
Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". Zenodo, 2022 - [i14]Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls:
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR abs/2206.15378 (2022) - [i13]Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments. CoRR abs/2209.10958 (2022) - [i12]Luke Marris, Ian Gemp, Thomas W. Anthony, Andrea Tacchetti, Siqi Liu, Karl Tuyls:
Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. CoRR abs/2210.09257 (2022) - 2021
- [b1]Thomas W. Anthony:
Expert iteration. University College London (University of London), UK, 2021 - [c7]Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Holger Buesing, Petar Velickovic, Theophane Weber:
On the role of planning in model-based deep reinforcement learning. ICLR 2021 - [c6]Max Olan Smith, Thomas Anthony, Michael P. Wellman:
Iterative Empirical Game Solving via Single Policy Best Response. ICLR 2021 - [c5]Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls:
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML 2021: 8525-8535 - [i11]Ian Gemp, Rahul Savani
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. CoRR abs/2106.01285 (2021) - [i10]Max Olan Smith, Thomas Anthony, Michael P. Wellman:
Iterative Empirical Game Solving via Single Policy Best Response. CoRR abs/2106.01901 (2021) - 2020
- [c4]Edward Hughes, Thomas W. Anthony, Tom Eccles, Joel Z. Leibo, David Balduzzi, Yoram Bachrach:
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games. AAMAS 2020: 538-547 - [c3]David Balduzzi, Wojciech M. Czarnecki, Tom Anthony, Ian Gemp, Edward Hughes, Joel Z. Leibo
Smooth markets: A basic mechanism for organizing gradient-based learners. ICLR 2020 - [c2]Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS 2020 - [i9]David Balduzzi, Wojciech M. Czarnecki, Thomas W. Anthony, Ian M. Gemp, Edward Hughes, Joel Z. Leibo, Georgios Piliouras, Thore Graepel:
Smooth markets: A basic mechanism for organizing gradient-based learners. CoRR abs/2001.04678 (2020) - [i8]Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls:
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR abs/2002.08456 (2020) - [i7]Edward Hughes, Thomas W. Anthony, Tom Eccles, Joel Z. Leibo, David Balduzzi, Yoram Bachrach:
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games. CoRR abs/2003.00799 (2020) - [i6]Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach:
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR abs/2006.04635 (2020) - [i5]Max Olan Smith, Thomas Anthony, Yongzhao Wang, Michael P. Wellman:
Learning to Play against Any Mixture of Opponents. CoRR abs/2009.14180 (2020) - [i4]Jessica B. Hamrick, Abram L. Friesen, Feryal M. P. Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Velickovic, Théophane Weber:
On the role of planning in model-based deep reinforcement learning. CoRR abs/2011.04021 (2020)
2010 – 2019
- 2019
- [i3]Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman:
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees. CoRR abs/1904.03646 (2019) - [i2]Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinícius Flores Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Paul Muller, Timo Ewalds
OpenSpiel: A Framework for Reinforcement Learning in Games. CoRR abs/1908.09453 (2019) - 2017
- [c1]Thomas Anthony, Zheng Tian, David Barber:
Thinking Fast and Slow with Deep Learning and Tree Search. NIPS 2017: 5360-5370 - [i1]Thomas Anthony, Zheng Tian, David Barber:
Thinking Fast and Slow with Deep Learning and Tree Search. CoRR abs/1705.08439 (2017)
Coauthor Index
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