Feature Construction for Reinforcement Learning in Hearts. - Nathan R. Sturtevant; Adam M. White (Computers and Games, 2006)

RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments. - Brian Tanner; Adam White 001 (J. Mach. Learn. Res., 2009)

Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains. - Martha White; Adam White 001 (NIPS, 2010)

Report on the 2008 Reinforcement Learning Competition. - Shimon Whiteson; Brian Tanner; Adam White 001 (AI Mag., 2010)

Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. - Richard S. Sutton; Joseph Modayil; Michael Delp; Thomas Degris; Patrick M. Pilarski; Adam White 001; Doina Precup (AAMAS, 2011)

Scaling life-long off-policy learning. - Adam White 001; Joseph Modayil; Richard S. Sutton (ICDL-EPIROB, 2012)

Acquiring a broad range of empirical knowledge in real time by temporal-difference learning. - Joseph Modayil; Adam White 001; Patrick M. Pilarski; Richard S. Sutton (SMC, 2012)

Multi-timescale nexting in a reinforcement learning robot. - Joseph Modayil; Adam White 001; Richard S. Sutton (Adapt. Behav., 2014)

Introspective Agents: Confidence Measures for General Value Functions. - Craig Sherstan; Adam White 001; Marlos C. Machado; Patrick M. Pilarski (AGI, 2016)

Investigating Practical Linear Temporal Difference Learning. - Adam White 001; Martha White (AAMAS, 2016)

A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning. - Martha White; Adam White 001 (AAMAS, 2016)

Accelerated Gradient Temporal Difference Learning. - Yangchen Pan; Adam White 001; Martha White (AAAI, 2017)

GQ($λ$) Quick Reference and Implementation Guide. - Adam White 001; Richard S. Sutton (CoRR, 2017)

Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains. - Yangchen Pan; Muhammad Zaheer; Adam White 001; Andrew Patterson; Martha White (IJCAI, 2018)

Context-dependent upper-confidence bounds for directed exploration. - Raksha Kumaraswamy; Matthew Schlegel; Adam White 001; Martha White (NeurIPS, 2018)

Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. - Craig Sherstan; Dylan R. Ashley; Brendan Bennett; Kenny Young; Adam White 001; Martha White; Richard S. Sutton (UAI, 2018)

Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods. - Craig Sherstan; Brendan Bennett; Kenny Young; Dylan R. Ashley; Adam White 001; Martha White; Richard S. Sutton (CoRR, 2018)

General Value Function Networks. - Matthew Schlegel; Adam White 001; Andrew Patterson; Martha White (CoRR, 2018)

Online Off-policy Prediction. - Sina Ghiassian; Andrew Patterson; Martha White; Richard S. Sutton; Adam White 001 (CoRR, 2018)

The Barbados 2018 List of Open Issues in Continual Learning. - Tom Schaul; Hado van Hasselt; Joseph Modayil; Martha White; Adam White 001; Pierre-Luc Bacon; Jean Harb; Shibl Mourad; Marc G. Bellemare; Doina Precup (CoRR, 2018)

Meta-Descent for Online, Continual Prediction. - Andrew Jacobsen; Matthew Schlegel; Cameron Linke; Thomas Degris; Adam White 001; Martha White (AAAI, 2019)

Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. - Banafsheh Rafiee; Sina Ghiassian; Adam White 001; Richard S. Sutton (AAMAS, 2019)

Planning with Expectation Models. - Yi Wan 004; Muhammad Zaheer; Adam White 001; Martha White; Richard S. Sutton (IJCAI, 2019)

Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study. - Cam Linke; Nadia M. Ady; Martha White; Thomas Degris; Adam White 001 (CoRR, 2019)

Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. - Sina Ghiassian; Banafsheh Rafiee; Yat Long Lo; Adam White 001 (AAMAS, 2020)

Training Recurrent Neural Networks Online by Learning Explicit State Variables. - Somjit Nath; Vincent Liu; Alan Chan 001; Xin Li; Adam White 001; Martha White (ICLR, 2020)

Gradient Temporal-Difference Learning with Regularized Corrections. - Sina Ghiassian; Andrew Patterson; Shivam Garg 006; Dhawal Gupta; Adam White 001; Martha White (ICML, 2020)

Towards a practical measure of interference for reinforcement learning. - Vincent Liu; Adam White 001; Hengshuai Yao; Martha White (CoRR, 2020)

Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study. - Cam Linke; Nadia M. Ady; Martha White; Thomas Degris; Adam White 001 (J. Artif. Intell. Res., 2020)

Emphatic Algorithms for Deep Reinforcement Learning. - Ray Jiang; Tom Zahavy; Zhongwen Xu; Adam White 001; Matteo Hessel; Charles Blundell; Hado van Hasselt (ICML, 2021)

Continual Auxiliary Task Learning. - Matthew McLeod; Chunlok Lo; Matthew Schlegel; Andrew Jacobsen; Raksha Kumaraswamy; Martha White; Adam White 001 (NeurIPS, 2021)

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. - Andrew Patterson; Adam White 001; Sina Ghiassian; Martha White (CoRR, 2021)

Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study. - Dylan J. A. Brenneis; Adam S. R. Parker; Michael Bradley Johanson; Andrew Butcher; Elnaz Davoodi; Leslie Acker; Matthew M. Botvinick; Joseph Modayil; Adam White 001; Patrick M. Pilarski (CoRR, 2021)

General Value Function Networks. - Matthew Schlegel; Andrew Jacobsen; Zaheer Abbas; Andrew Patterson; Adam White 001; Martha White (J. Artif. Intell. Res., 2021)

Learning Expected Emphatic Traces for Deep RL. - Ray Jiang; Shangtong Zhang; Veronica Chelu; Adam White 001; Hado van Hasselt (AAAI, 2022)

Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making. - Andrew Butcher; Michael Bradley Johanson; Elnaz Davoodi; Dylan J. A. Brenneis; Leslie Acker; Adam S. R. Parker; Adam White 001; Joseph Modayil; Patrick M. Pilarski (CoRR, 2022)

The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents. - Patrick M. Pilarski; Andrew Butcher; Elnaz Davoodi; Michael Bradley Johanson; Dylan J. A. Brenneis; Adam S. R. Parker; Leslie Acker; Matthew M. Botvinick; Joseph Modayil; Adam White 001 (CoRR, 2022)

What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy. - Banafsheh Rafiee; Jun Jin 001; Jun Luo 009; Adam White 001 (CoRR, 2022)

Goal-Space Planning with Subgoal Models. - Chunlok Lo; Gabor Mihucz; Adam White 001; Farzane Aminmansour; Martha White (CoRR, 2022)

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. - Andrew Patterson; Adam White 001; Martha White (J. Mach. Learn. Res., 2022)

No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL. - Han Wang; Archit Sakhadeo; Adam M. White; James Bell 004; Vincent Liu; Xutong Zhao; Puer Liu; Tadashi Kozuno; Alona Fyshe; Martha White (Trans. Mach. Learn. Res., 2022)

Entropy as a Measure of Puzzle Difficulty. - Eugene You Chen Chen; Adam White 001; Nathan R. Sturtevant (AIIDE, 2023)

Loss of Plasticity in Continual Deep Reinforcement Learning. - Zaheer Abbas; Rosie Zhao; Joseph Modayil; Adam White 001; Marlos C. Machado (CoLLAs, 2023)

Measuring and Mitigating Interference in Reinforcement Learning. - Vincent Liu; Han Wang; Ruo Yu Tao; Khurram Javed; Adam White 001; Martha White (CoLLAs, 2023)

Auxiliary task discovery through generate-and-test. - Banafsheh Rafiee; Sina Ghiassian; Jun Jin 001; Richard S. Sutton; Jun Luo 009; Adam White 001 (CoLLAs, 2023)

Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement. - Samuel Neumann; Sungsu Lim; Ajin George Joseph; Yangchen Pan; Adam White 001; Martha White (ICLR, 2023)

The In-Sample Softmax for Offline Reinforcement Learning. - Chenjun Xiao; Han Wang; Yangchen Pan; Adam White 001; Martha White (ICLR, 2023)

Empirical Design in Reinforcement Learning. - Andrew Patterson; Samuel Neumann; Martha White; Adam White 001 (CoRR, 2023)

Recurrent Linear Transformers. - Subhojeet Pramanik; Esraa Elelimy; Marlos C. Machado; Adam White 001 (CoRR, 2023)

Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning. - Matthew Schlegel; Volodymyr Tkachuk; Adam M. White; Martha White (Trans. Mach. Learn. Res., 2023)

Agent-State Construction with Auxiliary Inputs. - Ruo Yu Tao; Adam White 001; Marlos C. Machado (Trans. Mach. Learn. Res., 2023)

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. - Banafsheh Rafiee; Zaheer Abbas; Sina Ghiassian; Raksha Kumaraswamy; Richard S. Sutton; Elliot A. Ludvig; Adam White 001 (Adapt. Behav., 2023)

Reward-respecting subtasks for model-based reinforcement learning. - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (Artif. Intell., 2023)

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (AAAI, 2024)

Position: Benchmarking is Limited in Reinforcement Learning Research. - Scott M. Jordan; Adam White 001; Bruno Castro da Silva; Martha White; Philip S. Thomas (ICML, 2024)

A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning. - Jacob Adkins; Michael Bowling; Adam White 001 (NeurIPS, 2024)

Real-Time Recurrent Learning using Trace Units in Reinforcement Learning. - Esraa Elelimy; Adam White 001; Michael Bowling; Martha White (NeurIPS, 2024)

The Cliff of Overcommitment with Policy Gradient Step Sizes. - Scott M. Jordan; Samuel Neumann; James E. Kostas; Adam White 001; Philip S. Thomas (RLJ, 2024)

Harnessing Discrete Representations for Continual Reinforcement Learning. - Edan Meyer; Adam White 001; Marlos C. Machado (RLJ, 2024)

Investigating the Interplay of Prioritized Replay and Generalization. - Parham Mohammad Panahi; Andrew Patterson; Martha White; Adam White 001 (RLJ, 2024)

Cross-environment Hyperparameter Tuning for Reinforcement Learning. - Andrew Patterson; Samuel Neumann; Raksha Kumaraswamy; Martha White; Adam White 001 (RLJ, 2024)

Investigating the properties of neural network representations in reinforcement learning. - Han Wang; Erfan Miahi; Martha White; Marlos C. Machado; Zaheer Abbas; Raksha Kumaraswamy; Vincent Liu; Adam White 001 (Artif. Intell., 2024)

Tuning for the Unknown: Revisiting Evaluation Strategies for Lifelong RL. - Golnaz Mesbahi; Olya Mastikhina; Parham Mohammad Panahi; Martha White; Adam White 001 (CoRR, 2024)

A New View on Planning in Online Reinforcement Learning. - Kevin Roice; Parham Mohammad Panahi; Scott M. Jordan; Adam White 001; Martha White (CoRR, 2024)

The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. - Andrew Patterson; Samuel Neumann; Raksha Kumaraswamy; Martha White; Adam White 001 (CoRR, 2024)

AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning. - Subhojeet Pramanik; Esraa Elelimy; Marlos C. Machado; Adam White 001 (Trans. Mach. Learn. Res., 2024)

GVFs in the real world: making predictions online for water treatment. - Muhammad Kamran Janjua; Haseeb Shah; Martha White; Erfan Miahi; Marlos C. Machado; Adam White 001 (Mach. Learn., 2024)

Fine-Tuning without Performance Degradation. - Han Wang; Adam White 001; Martha White (CoRR, 2025)

Deep Reinforcement Learning with Gradient Eligibility Traces. - Esraa Elelimy; Brett Daley; Andrew Patterson; Marlos C. Machado; Adam White 001; Martha White (CoRR, 2025)