Dustin Morrill
Dustin Morrill
Computing Science PhD Candidate, University of Alberta and the Alberta Machine Intelligence
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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
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
Solving games with functional regret estimation
K Waugh, D Morrill, JA Bagnell, M Bowling
Twenty-ninth AAAI conference on artificial intelligence, 2015
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
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
Hindsight and sequential rationality of correlated play
D Morrill, R D'Orazio, R Sarfati, M Lanctot, JR Wright, A Greenwald, ...
arXiv preprint arXiv:2012.05874, 2020
Aivat: A new variance reduction technique for agent evaluation in imperfect information games
N Burch, M Schmid, M Moravcik, D Morill, M Bowling
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
Using regret estimation to solve games compactly
DR Morrill
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
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
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
Learning to Be Cautious
M Mohammedalamen, D Morrill, A Sieusahai, Y Satsangi, M Bowling
arXiv preprint arXiv:2110.15907, 2021
Bounds for approximate regret-matching algorithms
R D'Orazio, D Morrill, JR Wright
arXiv preprint arXiv:1910.01706, 2019
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
The Partially Observable History Process
D Morrill, AR Greenwald, M Bowling
arXiv preprint arXiv:2111.08102, 2021
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games Supplementary
D Morrill, R D’Orazio, M Lanctot, JR Wright, M Bowling, AR Greenwald
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