• Roberto Capobianco, Harm van Seijen, Nolan D. Bard, Neil Burch, Fatima Davelouis, Josh Davidson, Alisa Devlic, Yunshu Du, Ishan Durugkar, Siddhant Gangapurwala, Daniel Hernandez, G. Zacharias Holland, Sahil Jain, Kenta Kawamoto, Raksha Kumaraswamy, Patrick MacAlpine, Dustin Morrill, Declan Oller, Francesco Riccio, Akanksha Saran, Craig Sherstan, Kaushik Subramanian, Thomas J. Walsh, Samuel Barrett, Kizza N. Frisbee, Mady Govil, Johannes Günther, Varun R. Kompella, James A. MacGlashan, Maxwell Svetlik, Michael D. Thomure, Jaden B. Travnik, Kevin Waugh, Elahe Aghapour, Florian Fuchs, Andreanne Lemay, Shruti Mishra, Takuma Seno, Peter Stone, Michael Spranger, and Peter R. Wurman. Coachable Agents for Interactive Gameplay. arXiv, Jul 1, 2026.
• Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi, and Michael Bowling. Learning to Be Cautious. In Transactions on Machine Learning Research, Oct 1, 2025.
• Dustin Morrill, Thomas J. Walsh, Daniel Hernandez, Peter R. Wurman, and Peter Stone. Composing Efficient, Robust Tests for Policy Selection. At the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023), Jul 31, 2023. Pittsburgh, USA. [Acceptance Rate: 31.2%].
• Dustin Morrill. Hindsight Rational Learning for Sequential Decision-Making: Foundations and Experimental Applications. Ph.D. thesis, Department of Computing Science, University of Alberta, Sep 1, 2022. Edmonton, Canada.
• Dustin Morrill, Esra’a Saleh, Michael Bowling, and Amy Greenwald. Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration. Reinforcement Learning and Decision Making 2022, June 2022, Jun 8, 2022. Providence, Rhode Island, USA.
• Dustin Morrill, Amy Greenwald, and Michael Bowling. The Partially Observable History Process. AAAI-22 Workshop on Reinforcement Learning in Games, Feb 28, 2022. Vancouver, Canada.
• Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi, and Michael Bowling. Learning to Be Cautious. arXiv, Oct 29, 2021.
• Dustin Morrill, Ryan D’Orazio, Marc Lanctot, James R. Wright, Michael Bowling, and Amy Greenwald. Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games. At the Thirty-Eighth International Conference on Machine Learning (ICML 2021), Jul 18, 2021. Virtual. [Acceptance Rate: 21.5%].
• Dustin Morrill, Ryan D’Orazio, Reca Sarfati, Marc Lanctot, James R. Wright, Amy Greenwald, and Michael Bowling. Hindsight and Sequential Rationality of Correlated Play. At the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Feb 2, 2021. Virtual. [Acceptance Rate: 21.4%].
• Audrūnas Gruslys, Marc Lanctot, Rémi Munos, Finbarr Timbers, Martin Schmid, Julien Perolat, Dustin Morrill, Vinicius Zambaldi, Jean-Baptiste Lespiau, John Schultz, Mohammad Gheshlaghi Azar, Michael Bowling, and Karl Tuyls. The Advantage Regret-Matching Actor-Critic. arXiv, Aug 27, 2020.
• Daniel Hennes[1], Dustin Morrill[1], Shayegan Omidshafiei[1], Remi Munos, Julien Perolat, Marc Lanctot, Audrunas Gruslys, Jean-Baptiste Lespiau, Paavo Parmas, Edgar Duéñez-Guzmán, and Karl Tuyls. Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. At the Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2020), May 9, 2020. Auckland, New Zealand. [Acceptance Rate: 23.0%].
• Ryan D’Orazio[1], Dustin Morrill[1], James R. Wright, and Michael Bowling. Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression Counterfactual Regret Minimization. At the Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2020), May 9, 2020. Auckland, New Zealand. [Acceptance Rate: 23.0%].
• Ryan D’Orazio, Dustin Morrill, and James R. Wright. Bounds for Approximate Regret-Matching Algorithms. Smooth Games Optimization and Machine Learning Workshop: Bridging Game Theory and Deep Learning (SGO&ML) at NeurIPS 2019, Dec 14, 2019. Vancouver, Canada.
• Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinicius Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Paul Muller, Timo Ewalds, Ryan Faulkner, János Kramár, Bart De Vylder, Brennan Saeta, James Bradbury, David Ding, Sebastian Borgeaud, Matthew Lai, Julian Schrittwieser, Thomas Anthony, Edward Hughes, Ivo Danihelka, and Jonah Ryan-Davis. OpenSpiel: A Framework for Reinforcement Learning in Games. arXiv, Aug 26, 2019.
• Edward Lockhart, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr Timbers, and Karl Tuyls. Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent. At the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Aug 10, 2019. Macao, China. [Acceptance Rate: 17.9%].
• Neil Burch, Martin Schmid, Matej Moravčík, Dustin Morrill, and Michael Bowling. AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games. At the Thirty-Second AAAI Conference on Artificial Intelligence, Feb 2, 2018. New Orleans, USA. [Acceptance Rate: 24.6%].
• Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling. DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker. In Science, Mar 2, 2017.
• Dustin Morrill. Using Regret Estimation to Solve Games Compactly. M.Sc. thesis, Department of Computing Science, University of Alberta, Apr 1, 2016. Edmonton, Canada.
• Kevin Waugh, Dustin Morrill, J. Andrew Bagnell, and Michael Bowling. Solving Games with Functional Regret Estimation. At the Twenty-Ninth AAAI Conference on Artificial Intelligence, Jan 25, 2015. Austin, USA. Pages 2138–2145. [Acceptance Rate: 26.7%].

Last updated: Aug 13, 2026