Adam White

Journal Papers

• Théo Vincent, Kevin Gerhardt, Yogesh Tripathi, Habib Maraqten, Adam White, Martha White, Jan Peters, Carlo D’Eramo (2026). Gradient Iterated Temporal-Difference Learning. Reinforcement Learning Journal.
• Steven Tang, Eric Xiong, Anna Hakhverdyan, Andrew Patterson, Jacob Adkins, Jiamin He, Esraa Elelimy, Parham Mohammad Panahi, Martha White, Adam White (2026). Forager: a lightweight testbed for continual learning with partial observability in RL. Reinforcement Learning Journal.
• Ty Lazar, Matthew Vandergrift, Martha White, Adam White (2026). Revisiting FTA: A Sparse One-to-Many Activation for Reinforcement Learning. Reinforcement Learning Journal.
• Jordan Coblin, Han Wang, Martha White, Adam White (2026). Dynamics Models for Offline Hyperparameter Selection in Real-World RL. Reinforcement Learning Journal.
• Parham Mohammad Panahi, Armin Ashrafi, Hongming Du, Andrew Patterson, Martha White, Adam White (2026). Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning. Reinforcement Learning Journal.
• Samuel Neumann, Jiamin He, Adam White, Martha White (2025). Investigating the Utility of Mirror Descent in Off-policy Actor-Critic. Reinforcement Learning Journal.
• Thomas Ferguson, Alona Fyshe, Adam White (2025). Modelling human exploration with light-weight meta reinforcement learning algorithms. Reinforcement Learning Journal.
• Esraa Elelimy, Brett Daley, Andrew Patterson, Marlos C. Machado, Adam White, Michael Bowling, Martha White (2025). Deep Reinforcement Learning with Gradient Eligibility Traces. Reinforcement Learning Journal.
• Thomas Ferguson, Alona Fyshe, Adam White (2025). Electrophysiological signatures of the effect of context on exploration: Greater attentional and learning signals when exploration is costly. Brain Research.
• Subhojeet Pramanik, Esraa Elelimy, Marlos C. Machado, Adam White (2024). AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning. Transactions on Machine Learning Research.
• Andrew Patterson, Samuel Neumann, Martha White, Adam White (2024). Empirical Design in Reinforcement Learning. Journal of Machine Learning Research.
• Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White (2024). Goal-Space Planning with Subgoal Models. Journal of Machine Learning Research.
• Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas (2024). The Cliff of Overcommitment with Policy Gradient Step Sizes. Reinforcement Learning Journal.
• Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White (2024). Investigating the Interplay of Prioritized Replay and Generalization. Reinforcement Learning Journal.
• Edan Jacob Meyer, Adam White, Marlos C. Machado (2024). Harnessing Discrete Representations for Continual Reinforcement Learning. Reinforcement Learning Journal.
• Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White (2024). The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. Reinforcement Learning Journal.
• Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White (2024). Investigating the Properties of Neural Network Representations in Reinforcement Learning. Artificial Intelligence.
• Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White (2023). Reward-respecting subtasks for model-based reinforcement learning. Artificial Intelligence.
• Thomas D. Ferguson, Alona Fyshe, Adam White, Olave E. Krigolson (2023). Humans adopt different exploration strategies depending on the environment. Computational Brain & Behavior.
• Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White (2023). GVFs in the Real World: Making Predictions Online for Water Treatment. Machine Learning.
• Ruo Yu Tao, Marlos C. Machado, Adam White (2023). Agent-State Construction with Auxiliary Inputs. Transactions on Machine Learning Research.
• Matthew Schlegel, Volodymyr Tkachuk, Adam White, Martha White (2022). Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning. Transactions on Machine Learning Research.
• Han Wang, Archit Sakhadeo, Adam White, James M. Bell, Vincent Liu, Xutong Zhao, Paul Liu, Tadashi Kozuno, Alona Fyshe, Martha White (2022). No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL. Transactions on Machine Learning Research.
• Andrew Patterson, Adam White, Martha White (2022). A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. Journal of Machine Learning Research.
• Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy, Richard S. Sutton, Elliot Ludvig, Adam White (2022). From eye-blinks to state construction: diagnostic benchmarks for online representation learning. Adaptive Behavior.
• Matthew Schlegel, Andrew Jacobsen, Muhammad Zaheer, Andrew Patterson, Adam White, Martha White (2021). General value function networks. Journal of Artificial Intelligence Research.
• Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White (2020). Adapting behaviour via intrinsic reward: A survey and empirical study. Journal of Artificial Intelligence Research.
• Joseph Modayil, Adam White, Richard S. Sutton (2014). Multi-timescale Nexting in a Reinforcement Learning Robot. Adaptive Behavior, 22(2):146–160.
• Shimon Whiteson, Brian Tanner, Adam White (2010). The reinforcement learning competitions. AI Magazine, 31(2):81–94.
• Brian Tanner, Adam White (2009). RL-Glue: Language-independent software for reinforcement-learning experiments. Journal of Machine Learning Research, 10:2133–2136.

Conference Papers

• Golnaz Mesbahi, Parham Mohammad Panahi, Olya Mastikhina, Steven Tang, Martha White, Adam White (2025). Position: Lifetime tuning is incompatible with continual reinforcement learning. International Conference on Machine Learning.
• Jacob Adkins, Michael Bowling, Adam White (2024). A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning. Advances in Neural Information Processing Systems.
• Esraa Elelimy, Adam White, Michael Bowling, Martha White (2024). Real-Time Recurrent Learning using Trace Units in Reinforcement Learning. Advances in Neural Information Processing Systems.
• Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas (2024). Position: Benchmarking is Limited in Reinforcement Learning Research. International Conference on Machine Learning.
• David Rolnick, Alán Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White (2024). Position: Application-Driven Innovation in Machine Learning. International Conference on Machine Learning.
• Eugene Chen, Adam White, Nathan R. Sturtevant (2023). Entropy as a measure of puzzle difficulty. Artificial Intelligence and Interactive Digital Entertainment.
• Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado (2023). Loss of Plasticity in Continual Deep Reinforcement Learning. Conference on Lifelong Learning Agents.
• Vincent Liu, Han Wang, Ruo Yu Tao, Khurram Javed, Adam White, Martha White (2023). Measuring and Mitigating Interference in Reinforcement Learning. Conference on Lifelong Learning Agents.
• Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White (2023). Auxiliary task discovery through generate-and-test. Conference on Lifelong Learning Agents.
• Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White (2023). The In-Sample Softmax for Offline Reinforcement Learning. International Conference on Learning Representations.
• Samuel Neumann, Sungsu Lim, Ajin George Joseph, Yangchen Pan, Adam White, Martha White (2023). Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement. International Conference on Learning Representations.
• Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt (2022). Learning Expected Emphatic Traces for Deep RL. AAAI Conference on Artificial Intelligence.
• Matt McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White (2021). Continual auxiliary task learning. Advances in Neural Information Processing Systems.
• Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt (2021). Emphatic Algorithms for Deep Reinforcement Learning. International Conference on Machine Learning.
• Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White (2020). Gradient Temporal-Difference Learning with Regularized Corrections. International Conference on Machine Learning.
• Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White (2020). Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. International Conference on Autonomous Agents and Multi-Agent Systems.
• Somjit Nath, Vincent Liu, Alan Chan, Adam White, Martha White (2020). Training Recurrent Neural Networks Online by Learning Explicit State Variables. International Conference on Learning Representations.
• Yi Wan, Muhammad Zaheer, Richard S. Sutton, Adam White, Martha White (2019). Planning with Expectation Models. International Joint Conference on Artificial Intelligence.
• Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard S. Sutton (2019). Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. International Conference on Autonomous Agents and Multiagent Systems.
• Andrew Jacobsen, Matthew Schlegel, Cam Linke, Thomas Degris, Adam White, Martha White (2019). Meta-descent for online, continual prediction. AAAI Conference on Artificial Intelligence.
• Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White (2018). Context-dependent upper-confidence bounds for directed exploration. Advances in Neural Information Processing Systems.
• Craig Sherstan, Brendan Bennett, Kenny Young, Dylan Ashley, Adam White, Martha White, Richard S. Sutton (2018). Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. Conference on Uncertainty in Artificial Intelligence.
• Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White (2018). Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains. International Joint Conference on Artificial Intelligence.
• Yangchen Pan, Adam White, Martha White (2017). Accelerated Gradient Temporal Difference Learning. AAAI Conference on Artificial Intelligence.
• Craig Sherstan, Marlos C. Machado, Adam White, Patrick M. Pilarski (2016). Introspective Agents: Confidence Measures for General Value Functions. Artificial General Intelligence.
• Adam White, Martha White (2016). Investigating practical linear temporal difference learning. International Conference on Autonomous Agents and MultiAgent Systems. [Code]
• Martha White, Adam White (2016). A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning. International Conference on Autonomous Agents and MultiAgent Systems.
• Adam White, Joseph Modayil, Richard S. Sutton (2012). Scaling life-long off-policy learning. IEEE International Conference on Development and Learning and Epigenetic Robotics. Paper of distinction award
• Joseph Modayil, Adam White, Patrick M. Pilarski, Richard S. Sutton (2012). Acquiring a broad range of empirical knowledge in real time by temporal-difference learning. IEEE International Conference on Systems, Man, and Cybernetics.
• Joseph Modayil, Adam White, Richard S. Sutton (2012). Multi-timescale Nexting in a Reinforcement Learning Robot. International Conference on Adaptive Behaviour.
• Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, Doina Precup (2011). Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. International Conference on Autonomous Agents and Multiagent Systems.
• Martha White, Adam White (2010). Interval estimation for reinforcement-learning algorithms in continuous-state domains. Advances in Neural Information Processing Systems.
• Nathan R. Sturtevant, Adam M. White (2007). Feature construction for reinforcement learning in hearts. Computers and Games.

Preprints

• Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White (2019). Online Off-policy Prediction. arXiv.

Other Published Works

• Niko Yasui, Sungsu Lim, Cam Linke, Adam White, Martha White (2019). An Empirical and Conceptual Categorization of Value-based Exploration Methods. ICML Exploration in RL Workshop.
• Yangchen Pan, Adam White, Martha White (2017). Accelerated Gradient Temporal Difference Learning. European Workshop on Reinforcement Learning.
• Matthew Schlegel, Adam White, Martha White (2017). Stable predictive representations with general value functions for continual learning. Continual Learning and Deep Networks Workshop, NeurIPS.
• Adam White, Richard S. Sutton (2014). GQ(lambda) Quick Reference Guide.
• Adam White, Joseph Modayil, Richard S. Sutton (2014). Surprise and curiosity for big data robotics. AAAI Workshops.
• Joseph Modayil, Adam White, Patrick M. Pilarski, Richard S. Sutton (2012). Acquiring Diverse Predictive Knowledge in Real Time by Temporal-difference Learning. ERLARS Workshop. Best paper award
• Joseph Modayil, Patrick M. Pilarski, Adam White, Thomas Degris, Richard S. Sutton (2010). Off-policy knowledge maintenance for robots. RSS Workshop.

Theses

• Adam White (2015). Developing a predictive approach to knowledge. Doctoral thesis, University of Alberta.
• Adam White (2006). A standard system for benchmarking in reinforcement learning. Master’s thesis, University of Alberta.

Postdoctoral Alumni

PhD Alumni

MSc Alumni