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Finbarr Timbers
Finbarr Timbers
Google DeepMind
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Title
Cited by
Cited by
Year
OpenSpiel: A framework for reinforcement learning in games
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
128*2019
Detecting duplicate bug reports with software engineering domain knowledge
K Aggarwal, F Timbers, T Rutgers, A Hindle, E Stroulia, R Greiner
Journal of Software: Evolution and Process 29 (3), e1821, 2017
772017
Computing approximate equilibria in sequential adversarial games by exploitability descent
E Lockhart, M Lanctot, J Pérolat, JB Lespiau, D Morrill, F Timbers, K Tuyls
arXiv preprint arXiv:1903.05614, 2019
442019
Player of games
M Schmid, M Moravcik, N Burch, R Kadlec, J Davidson, K Waugh, N Bard, ...
arXiv preprint arXiv:2112.03178, 2021
132021
The advantage regret-matching actor-critic
A Gruslys, M Lanctot, R Munos, F Timbers, M Schmid, J Perolat, D Morrill, ...
arXiv preprint arXiv:2008.12234, 2020
112020
Solving common-payoff games with approximate policy iteration
S Sokota, E Lockhart, F Timbers, E Davoodi, R D'Orazio, N Burch, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9695-9703, 2021
102021
Bug reports dataset
A Alipour, A Hindle, T Rutgers, R Dawson, F Timbers, K Aggarwal
102013
Approximate exploitability: Learning a best response in large games
F Timbers, E Lockhart, M Lanctot, M Schmid, J Schrittwieser, T Hubert, ...
arXiv preprint arXiv:2004.09677, 2020
72020
Fast computation of nash equilibria in imperfect information games
R Munos, J Perolat, JB Lespiau, M Rowland, B De Vylder, M Lanctot, ...
International Conference on Machine Learning, 7119-7129, 2020
62020
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
MB Johanson, E Hughes, F Timbers, JZ Leibo
arXiv preprint arXiv:2205.06760, 2022
12022
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
J Perolat, B de Vylder, D Hennes, E Tarassov, F Strub, V de Boer, ...
arXiv preprint arXiv:2206.15378, 2022
2022
Approximate Exploitability: Learning a Best Response
F Timbers, N Bard, E Lockhart, M Lanctot, M Schmid, N Burch, ...
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