| Deepstack: Expert-level artificial intelligence in heads-up no-limit poker M Moravčík, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ... Science 356 (6337), 508-513, 2017 | 1144 | 2017 |
| 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 |
| 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 |
| Solving games with functional regret estimation K Waugh, D Morrill, J Bagnell, M Bowling Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 66 | 2015 |
| Neural replicator dynamics: Multiagent learning via hedging policy gradients D Hennes, D Morrill, S Omidshafiei, R Munos, J Perolat, M Lanctot, ... Proceedings of the 19th international conference on autonomous agents and …, 2020 | 46 | 2020 |
| Hindsight and sequential rationality of correlated play D Morrill, R D'Orazio, R Sarfati, M Lanctot, JR Wright, AR Greenwald, ... Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 5584-5594, 2021 | 33 | 2021 |
| Efficient deviation types and learning for hindsight rationality in extensive-form games D Morrill, R D’Orazio, M Lanctot, JR Wright, M Bowling, AR Greenwald International Conference on Machine Learning, 7818-7828, 2021 | 32 | 2021 |
| 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 |
| Neural replicator dynamics D Hennes, D Morrill, S Omidshafiei, R Munos, J Perolat, M Lanctot, ... arXiv preprint arXiv:1906.00190, 2019 | 23 | 2019 |
| 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 |
| Using regret estimation to solve games compactly DR Morrill | 18 | 2016 |
| Alternative Function Approximation Parameterizations for Solving Games: An Analysis of -Regression Counterfactual Regret Minimization R D'Orazio, D Morrill, JR Wright, M Bowling arXiv preprint arXiv:1912.02967, 2019 | 11 | 2019 |
| Neural replicator dynamics S Omidshafiei, D Hennes, D Morrill, R Munos, J Perolat, M Lanctot, ... arXiv preprint arXiv:1906.00190, 2019 | 11 | 2019 |
| Learning to Be Cautious M Mohammedalamen, D Morrill, A Sieusahai, Y Satsangi, M Bowling arXiv preprint arXiv:2110.15907, 2021 | 3 | 2021 |
| Deepstack: expert-level artificial intelligence in no-limit poker. CoRR abs/1701.01724 (2017) M Moravcık, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ... | 3 | |
| Hindsight rational learning for sequential decision-making: Foundations and experimental applications D Morrill | 2 | 2022 |
| The Partially Observable History Process D Morrill, AR Greenwald, M Bowling arXiv preprint arXiv:2111.08102, 2021 | 2 | 2021 |
| Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections D Morrill, R D'Orazio, M Lanctot, JR Wright, M Bowling, AR Greenwald arXiv preprint arXiv:2205.12031, 2022 | 1 | 2022 |
| Bounds for approximate regret-matching algorithms R D'Orazio, D Morrill, JR Wright arXiv preprint arXiv:1910.01706, 2019 | 1 | 2019 |
| Composing efficient, robust tests for policy selection D Morrill, TJ Walsh, D Hernandez, PR Wurman, P Stone Uncertainty in Artificial Intelligence, 1456-1466, 2023 | | 2023 |
