Thinking Fast and Slow with Deep Learning and Tree Search. - Thomas Anthony 001; Zheng Tian 002; David Barber (NIPS, 2017)

Policy Gradient Search: Online Planning and Expert Iteration without Search Trees. - Thomas Anthony 001; Robert Nishihara; Philipp Moritz; Tim Salimans; John Schulman (CoRR, 2019)

OpenSpiel: A Framework for Reinforcement Learning in Games. - Marc Lanctot; Edward Lockhart; Jean-Baptiste Lespiau; Vinícius Flores Zambaldi; Satyaki Upadhyay; Julien Pérolat; Sriram Srinivasan 005; Finbarr Timbers; Karl Tuyls; Shayegan Omidshafiei; Daniel Hennes; Dustin Morrill; et al. (CoRR, 2019)

Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games. - Edward Hughes 001; Thomas W. Anthony 001; Tom Eccles; Joel Z. Leibo; David Balduzzi; Yoram Bachrach (AAMAS, 2020)

Smooth markets: A basic mechanism for organizing gradient-based learners. - David Balduzzi; Wojciech M. Czarnecki; Tom Anthony 001; Ian Gemp; Edward Hughes 001; Joel Z. Leibo; Georgios Piliouras; Thore Graepel (ICLR, 2020)

Learning to Play No-Press Diplomacy with Best Response Policy Iteration. - Thomas W. Anthony 001; Tom Eccles; Andrea Tacchetti; János Kramár; Ian Gemp; Thomas C. Hudson; Nicolas Porcel; Marc Lanctot; Julien Pérolat; Richard Everett 001; Satinder Singh 001; Thore Graepel; et al. (NeurIPS, 2020)

Learning to Play against Any Mixture of Opponents. - Max Olan Smith; Thomas Anthony 001; Yongzhao Wang 001; Michael P. Wellman (CoRR, 2020)

On the role of planning in model-based deep reinforcement learning. - Jessica B. Hamrick; Abram L. Friesen; Feryal M. P. Behbahani; Arthur Guez; Fabio Viola; Sims Witherspoon; Thomas Anthony 001; Lars Holger Buesing; Petar Velickovic; Theophane Weber (ICLR, 2021)

Iterative Empirical Game Solving via Single Policy Best Response. - Max Olan Smith; Thomas Anthony 001; Michael P. Wellman (ICLR, 2021)

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. - Julien Pérolat; Rémi Munos; Jean-Baptiste Lespiau; Shayegan Omidshafiei; Mark Rowland 001; Pedro A. Ortega; Neil Burch; Thomas W. Anthony 001; David Balduzzi; Bart De Vylder; Georgios Piliouras; Marc Lanctot; et al. (ICML, 2021)

Expert iteration - Thomas W. Anthony 001 (2021)

Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. - Ian Gemp; Rahul Savani; Marc Lanctot; Yoram Bachrach; Thomas W. Anthony 001; Richard Everett 001; Andrea Tacchetti; Tom Eccles; János Kramár (AAMAS, 2022)

Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers. - Luke Marris; Ian Gemp; Thomas Anthony 001; Andrea Tacchetti; Siqi Liu 002; Karl Tuyls (NeurIPS, 2022)

Developing, evaluating and scaling learning agents in multi-agent environments. - Ian Gemp; Thomas W. Anthony 001; Yoram Bachrach; Avishkar Bhoopchand; Kalesha Bullard; Jerome T. Connor; Vibhavari Dasagi; Bart De Vylder; Edgar A. Duéñez-Guzmán; Romuald Elie; Richard Everett 001; Daniel Hennes; et al. (AI Commun., 2022)

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas W. Anthony 001; Stephen McAleer; Romuald Elie; et al. (CoRR, 2022)

Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas Anthony 001; Stephen McAleer; Romuald Elie; et al. (Zenodo, 2022)

Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas. - Udari Madhushani; Kevin R. McKee; John P. Agapiou; Joel Z. Leibo; Richard Everett 001; Thomas W. Anthony 001; Edward Hughes 001; Karl Tuyls; Edgar A. Duéñez-Guzmán (CoRR, 2023)

Evaluating Agents using Social Choice Theory. - Marc Lanctot; Kate Larson; Yoram Bachrach; Luke Marris; Zun Li 002; Avishkar Bhoopchand; Thomas W. Anthony 001; Brian Tanner; Anna Koop (CoRR, 2023)

Strategic Knowledge Transfer. - Max Olan Smith; Thomas W. Anthony 001; Michael P. Wellman (J. Mach. Learn. Res., 2023)

Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. - Marc Lanctot; John Schultz; Neil Burch; Max Olan Smith; Daniel Hennes; Thomas Anthony 001; Julien Pérolat (Trans. Mach. Learn. Res., 2023)

Learning to play against any mixture of opponents. - Max Olan Smith; Thomas W. Anthony 001; Michael P. Wellman (Frontiers Artif. Intell., 2023)