| 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 | 234 | 2019 |
| 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, ... Science 378 (6623), 990-996, 2022 | 145 | 2022 |
| 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 | 114 | 2017 |
| 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 | 73 | 2019 |
| Player of games M Schmid, M Moravcik, N Burch, R Kadlec, J Davidson, K Waugh, N Bard, ... arXiv preprint arXiv:2112.03178, 2021 | 48 | 2021 |
| Approximate exploitability: Learning a best response in large games F Timbers, N Bard, E Lockhart, M Lanctot, M Schmid, N Burch, ... arXiv preprint arXiv:2004.09677, 2020 | 36 | 2020 |
| 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 | 24 | 2020 |
| OpenSpiel: a framework for reinforcement learning in games. CoRR abs/1908.09453 (2019) M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ... arXiv preprint arXiv:1908.09453, 2019 | 23 | 2019 |
| Emergent bartering behaviour in multi-agent reinforcement learning MB Johanson, E Hughes, F Timbers, JZ Leibo arXiv preprint arXiv:2205.06760, 2022 | 20 | 2022 |
| Reward-respecting subtasks for model-based reinforcement learning RS Sutton, MC Machado, GZ Holland, D Szepesvari, F Timbers, B Tanner, ... Artificial Intelligence 324, 104001, 2023 | 19 | 2023 |
| 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 | 14 | 2021 |
| 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 | 12 | 2020 |
| Bug reports dataset A Alipour, A Hindle, T Rutgers, R Dawson, F Timbers, K Aggarwal Dedup, 2013 | 11 | 2013 |
| Student of Games: A unified learning algorithm for both perfect and imperfect information games M Schmid, M Moravčík, N Burch, R Kadlec, J Davidson, K Waugh, N Bard, ... Science Advances 9 (46), eadg3256, 2023 | 4 | 2023 |
| Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint) RS Sutton, MC Machado, GZ Holland, D Szepesvari, F Timbers, B Tanner, ... Proceedings of the AAAI Conference on Artificial Intelligence 38 (20), 22713 …, 2024 | | 2024 |
| Detecting Duplicate Bug Reports using a Hierarchy of Domain Knowledge Contexts RG K. Aggarwal, A. Hindle, F. Timbers, E. Stroulia, T. Rutgers Journal of Software: Evolution and Process 29 (3), 2017 | | 2017 |
Marc LanctotResearch Scientist, Google DeepMindVerifierad e-postadress på google.com
