Detecting duplicate bug reports with software engineering domain knowledge. - Karan Aggarwal; Finbarr Timbers; Tanner Rutgers; Abram Hindle; Eleni Stroulia; Russell Greiner (J. Softw. Evol. Process., 2017)

Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent. - Edward Lockhart; Marc Lanctot; Julien Pérolat; Jean-Baptiste Lespiau; Dustin Morrill; Finbarr Timbers; Karl Tuyls (IJCAI, 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)

Fast computation of Nash Equilibria in Imperfect Information Games. - Rémi Munos; Julien Pérolat; Jean-Baptiste Lespiau; Mark Rowland 001; Bart De Vylder; Marc Lanctot; Finbarr Timbers; Daniel Hennes; Shayegan Omidshafiei; Audrunas Gruslys; Mohammad Gheshlaghi Azar; Edward Lockhart; et al. (ICML, 2020)

Approximate exploitability: Learning a best response in large games. - Finbarr Timbers; Edward Lockhart; Martin Schmid; Marc Lanctot; Michael Bowling (CoRR, 2020)

The Advantage Regret-Matching Actor-Critic. - Audrunas Gruslys; Marc Lanctot; Rémi Munos; Finbarr Timbers; Martin Schmid; Julien Pérolat; Dustin Morrill; Vinícius Flores Zambaldi; Jean-Baptiste Lespiau; John Schultz; Mohammad Gheshlaghi Azar; Michael Bowling; et al. (CoRR, 2020)

Solving Common-Payoff Games with Approximate Policy Iteration. - Samuel Sokota; Edward Lockhart; Finbarr Timbers; Elnaz Davoodi; Ryan D'Orazio; Neil Burch; Martin Schmid; Michael Bowling; Marc Lanctot (AAAI, 2021)

Player of Games. - Martin Schmid; Matej Moravcik; Neil Burch; Rudolf Kadlec; Joshua Davidson; Kevin Waugh; Nolan Bard; Finbarr Timbers; Marc Lanctot; G. Zacharias Holland; Elnaz Davoodi; Alden Christianson; et al. (CoRR, 2021)

Approximate Exploitability: Learning a Best Response. - Finbarr Timbers; Nolan Bard; Edward Lockhart; Marc Lanctot; Martin Schmid; Neil Burch; Julian Schrittwieser; Thomas Hubert; Michael Bowling (IJCAI, 2022)

Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning. - Michael Bradley Johanson; Edward Hughes 001; Finbarr Timbers; Joel Z. Leibo (CoRR, 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)

Reward-respecting subtasks for model-based reinforcement learning. - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (Artif. Intell., 2023)

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (AAAI, 2024)