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Adam White 0001
Adam M. White
Person information
- affiliation: DeepMind Ltd, Edmonton, AB, Canada
- affiliation: Indiana University at Bloomington, Department of Computer Science, IN, USA
- affiliation (PhD 2015): University of Alberta, Department of Computing Science, Edmonton, AB, Canada
Other persons with the same name
- Adam White — disambiguation page
- Adam White 0002
— City University of London, City Data Science Institute, UK
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2020 – today
- 2024
- [j17]Edan Meyer, Adam White, Marlos C. Machado:
Harnessing Discrete Representations for Continual Reinforcement Learning. RLJ 2: 606-628 (2024) - [j16]Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas:
The Cliff of Overcommitment with Policy Gradient Step Sizes. RLJ 2: 864-883 (2024) - [j15]Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White:
Investigating the Interplay of Prioritized Replay and Generalization. RLJ 5: 2041-2058 (2024) - [j14]Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White:
Cross-environment Hyperparameter Tuning for Reinforcement Learning. RLJ 5: 2298-2319 (2024) - [j13]Han Wang
Investigating the properties of neural network representations in reinforcement learning. Artif. Intell. 330: 104100 (2024) - [j12]Muhammad Kamran Janjua, Haseeb Shah
GVFs in the real world: making predictions online for water treatment. Mach. Learn. 113(8): 5151-5181 (2024) - [c30]Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White:
Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). AAAI 2024: 22713 - [c29]Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas:
Position: Benchmarking is Limited in Reinforcement Learning Research. ICML 2024 - [i46]Golnaz Mesbahi, Olya Mastikhina, Parham Mohammad Panahi, Martha White, Adam White:
Tuning for the Unknown: Revisiting Evaluation Strategies for Lifelong RL. CoRR abs/2404.02113 (2024) - [i45]Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White:
A New View on Planning in Online Reinforcement Learning. CoRR abs/2406.01562 (2024) - [i44]Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas:
Position: Benchmarking is Limited in Reinforcement Learning Research. CoRR abs/2406.16241 (2024) - [i43]Parham Mohammad Panahi, Andrew Patterson, Martha White, Adam White:
Investigating the Interplay of Prioritized Replay and Generalization. CoRR abs/2407.09702 (2024) - [i42]Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy, Martha White, Adam White:
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning. CoRR abs/2407.18840 (2024) - [i41]Esraa Elelimy, Adam White, Michael Bowling, Martha White:
Real-Time Recurrent Learning using Trace Units in Reinforcement Learning. CoRR abs/2409.01449 (2024) - 2023
- [j11]Banafsheh Rafiee
From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. Adapt. Behav. 31(1): 3-19 (2023) - [j10]Richard S. Sutton, Marlos C. Machado
Reward-respecting subtasks for model-based reinforcement learning. Artif. Intell. 324: 104001 (2023) - [j9]Matthew Schlegel, Volodymyr Tkachuk, Adam M. White, Martha White:
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j8]Ruo Yu Tao, Adam White, Marlos C. Machado:
Agent-State Construction with Auxiliary Inputs. Trans. Mach. Learn. Res. 2023 (2023) - [c28]Eugene You Chen Chen, Adam White, Nathan R. Sturtevant:
Entropy as a Measure of Puzzle Difficulty. AIIDE 2023: 34-42 - [c27]Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado:
Loss of Plasticity in Continual Deep Reinforcement Learning. CoLLAs 2023: 620-636 - [c26]Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White:
Auxiliary task discovery through generate-and-test. CoLLAs 2023: 703-714 - [c25]Vincent Liu, Han Wang, Ruo Yu Tao, Khurram Javed, Adam White, Martha White:
Measuring and Mitigating Interference in Reinforcement Learning. CoLLAs 2023: 781-795 - [c24]Samuel Neumann, Sungsu Lim, Ajin George Joseph, Yangchen Pan, Adam White, Martha White:
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement. ICLR 2023 - [c23]Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White:
The In-Sample Softmax for Offline Reinforcement Learning. ICLR 2023 - [i40]Chenjun Xiao, Han Wang, Yangchen Pan
The In-Sample Softmax for Offline Reinforcement Learning. CoRR abs/2302.14372 (2023) - [i39]Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado:
Loss of Plasticity in Continual Deep Reinforcement Learning. CoRR abs/2303.07507 (2023) - [i38]Andrew Patterson, Samuel Neumann, Martha White, Adam White:
Empirical Design in Reinforcement Learning. CoRR abs/2304.01315 (2023) - [i37]Vincent Liu, Han Wang, Ruo Yu Tao, Khurram Javed, Adam White, Martha White:
Measuring and Mitigating Interference in Reinforcement Learning. CoRR abs/2307.04887 (2023) - [i36]Subhojeet Pramanik, Esraa Elelimy, Marlos C. Machado, Adam White:
Recurrent Linear Transformers. CoRR abs/2310.15719 (2023) - [i35]Edan Meyer, Adam White, Marlos C. Machado:
Harnessing Discrete Representations For Continual Reinforcement Learning. CoRR abs/2312.01203 (2023) - [i34]Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White:
GVFs in the Real World: Making Predictions Online for Water Treatment. CoRR abs/2312.01624 (2023) - 2022
- [j7]Andrew Patterson, Adam White, Martha White:
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. J. Mach. Learn. Res. 23: 145:1-145:61 (2022) - [j6]Han Wang, Archit Sakhadeo, Adam M. White, James Bell, Vincent Liu, Xutong Zhao, Puer Liu, Tadashi Kozuno, Alona Fyshe, Martha White:
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL. Trans. Mach. Learn. Res. 2022 (2022) - [c22]Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt:
Learning Expected Emphatic Traces for Deep RL. AAAI 2022: 7015-7023 - [i33]Andrew Butcher, Michael Bradley Johanson, Elnaz Davoodi, Dylan J. A. Brenneis, Leslie Acker, Adam S. R. Parker, Adam White, Joseph Modayil, Patrick M. Pilarski:
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making. CoRR abs/2201.03709 (2022) - [i32]Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White:
Reward-Respecting Subtasks for Model-Based Reinforcement Learning. CoRR abs/2202.03466 (2022) - [i31]Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White:
Continual Auxiliary Task Learning. CoRR abs/2202.11133 (2022) - [i30]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:
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents. CoRR abs/2203.09498 (2022) - [i29]Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White:
Investigating the Properties of Neural Network Representations in Reinforcement Learning. CoRR abs/2203.15955 (2022) - [i28]Banafsheh Rafiee, Jun Jin, Jun Luo, Adam White:
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy. CoRR abs/2204.00565 (2022) - [i27]Han Wang, Archit Sakhadeo, Adam White, James Bell, Vincent Liu, Xutong Zhao, Puer Liu, Tadashi Kozuno, Alona Fyshe
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL. CoRR abs/2205.08716 (2022) - [i26]Chunlok Lo, Gabor Mihucz, Adam White, Farzane Aminmansour, Martha White:
Goal-Space Planning with Subgoal Models. CoRR abs/2206.02902 (2022) - [i25]Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White:
Auxiliary task discovery through generate-and-test. CoRR abs/2210.14361 (2022) - [i24]Ruo Yu Tao, Adam White, Marlos C. Machado:
Agent-State Construction with Auxiliary Inputs. CoRR abs/2211.07805 (2022) - 2021
- [j5]Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas, Andrew Patterson, Adam White, Martha White:
General Value Function Networks. J. Artif. Intell. Res. 70: 497-543 (2021) - [c21]Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt:
Emphatic Algorithms for Deep Reinforcement Learning. ICML 2021: 5023-5033 - [c20]Matthew McLeod, Chunlok Lo, Matthew Schlegel, Andrew Jacobsen, Raksha Kumaraswamy, Martha White, Adam White:
Continual Auxiliary Task Learning. NeurIPS 2021: 12549-12562 - [i23]Andrew Patterson, Adam White, Sina Ghiassian, Martha White:
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning. CoRR abs/2104.13844 (2021) - [i22]Ray Jiang, Tom Zahavy, Zhongwen Xu, Adam White, Matteo Hessel, Charles Blundell, Hado van Hasselt:
Emphatic Algorithms for Deep Reinforcement Learning. CoRR abs/2106.11779 (2021) - [i21]Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt:
Learning Expected Emphatic Traces for Deep RL. CoRR abs/2107.05405 (2021) - [i20]Dylan J. A. Brenneis, Adam S. R. Parker, Michael Bradley Johanson, Andrew Butcher, Elnaz Davoodi, Leslie Acker, Matthew M. Botvinick, Joseph Modayil, Adam White, Patrick M. Pilarski:
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study. CoRR abs/2112.07774 (2021) - 2020
- [j4]Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White:
Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study. J. Artif. Intell. Res. 69: 1287-1332 (2020) - [c19]Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White:
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. AAMAS 2020: 438-446 - [c18]Somjit Nath, Vincent Liu, Alan Chan, Xin Li, Adam White, Martha White:
Training Recurrent Neural Networks Online by Learning Explicit State Variables. ICLR 2020 - [c17]Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White:
Gradient Temporal-Difference Learning with Regularized Corrections. ICML 2020: 3524-3534 - [i19]Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo, Adam White:
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks. CoRR abs/2003.07417 (2020) - [i18]Sina Ghiassian, Andrew Patterson, Shivam Garg, Dhawal Gupta, Adam White, Martha White:
Gradient Temporal-Difference Learning with Regularized Corrections. CoRR abs/2007.00611 (2020) - [i17]Vincent Liu, Adam White, Hengshuai Yao, Martha White:
Towards a practical measure of interference for reinforcement learning. CoRR abs/2007.03807 (2020)
2010 – 2019
- 2019
- [c16]Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White:
Meta-Descent for Online, Continual Prediction. AAAI 2019: 3943-3950 - [c15]Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard S. Sutton:
Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. AAMAS 2019: 332-340 - [c14]Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton:
Planning with Expectation Models. IJCAI 2019: 3649-3655 - [i16]Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton:
Planning with Expectation Models. CoRR abs/1904.01191 (2019) - [i15]Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White:
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study. CoRR abs/1906.07865 (2019) - [i14]Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White:
Meta-descent for Online, Continual Prediction. CoRR abs/1907.07751 (2019) - 2018
- [c13]Yangchen Pan
Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains. IJCAI 2018: 4794-4800 - [c12]Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White:
Context-dependent upper-confidence bounds for directed exploration. NeurIPS 2018: 4784-4794 - [c11]Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton:
Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. UAI 2018: 63-72 - [i13]Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods. CoRR abs/1801.08287 (2018) - [i12]Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White:
Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains. CoRR abs/1806.04624 (2018) - [i11]Matthew Schlegel, Adam White, Andrew Patterson, Martha White:
General Value Function Networks. CoRR abs/1807.06763 (2018) - [i10]Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White:
Online Off-policy Prediction. CoRR abs/1811.02597 (2018) - [i9]Raksha Kumaraswamy, Matthew Schlegel, Adam White, Martha White:
Context-Dependent Upper-Confidence Bounds for Directed Exploration. CoRR abs/1811.06629 (2018) - [i8]Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup:
The Barbados 2018 List of Open Issues in Continual Learning. CoRR abs/1811.07004 (2018) - 2017
- [c10]Yangchen Pan
Accelerated Gradient Temporal Difference Learning. AAAI 2017: 2464-2470 - [i7]Adam White, Richard S. Sutton:
GQ($λ$) Quick Reference and Implementation Guide. CoRR abs/1705.03967 (2017) - 2016
- [c9]Craig Sherstan, Adam White, Marlos C. Machado, Patrick M. Pilarski
Introspective Agents: Confidence Measures for General Value Functions. AGI 2016: 258-261 - [c8]Adam White, Martha White:
Investigating Practical Linear Temporal Difference Learning. AAMAS 2016: 494-502 - [c7]Martha White, Adam White:
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning. AAMAS 2016: 557-565 - [i6]Adam White, Martha White:
Investigating practical, linear temporal difference learning. CoRR abs/1602.08771 (2016) - [i5]Craig Sherstan, Adam White, Marlos C. Machado, Patrick M. Pilarski:
Introspective Agents: Confidence Measures for General Value Functions. CoRR abs/1606.05593 (2016) - [i4]Martha White, Adam White:
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning. CoRR abs/1607.00446 (2016) - [i3]Yangchen Pan, Adam White, Martha White:
Accelerated Gradient Temporal Difference Learning. CoRR abs/1611.09328 (2016) - 2014
- [j3]Joseph Modayil, Adam White, Richard S. Sutton:
Multi-timescale nexting in a reinforcement learning robot. Adapt. Behav. 22(2): 146-160 (2014) - 2012
- [c6]Adam White, Joseph Modayil, Richard S. Sutton:
Scaling life-long off-policy learning. ICDL-EPIROB 2012: 1-6 - [c5]Joseph Modayil, Adam White, Richard S. Sutton:
Multi-timescale Nexting in a Reinforcement Learning Robot. SAB 2012: 299-309 - [c4]Joseph Modayil, Adam White, Patrick M. Pilarski
Acquiring a broad range of empirical knowledge in real time by temporal-difference learning. SMC 2012: 1903-1910 - [i2]Adam White, Joseph Modayil, Richard S. Sutton:
Scaling Life-long Off-policy Learning. CoRR abs/1206.6262 (2012) - 2011
- [c3]Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, Doina Precup:
Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. AAMAS 2011: 761-768 - [i1]Joseph Modayil, Adam White, Richard S. Sutton:
Multi-timescale Nexting in a Reinforcement Learning Robot. CoRR abs/1112.1133 (2011) - 2010
- [j2]Shimon Whiteson, Brian Tanner, Adam White:
Report on the 2008 Reinforcement Learning Competition. AI Mag. 31(2): 81-94 (2010) - [c2]Martha White, Adam White:
Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains. NIPS 2010: 2433-2441
2000 – 2009
- 2009
- [j1]Brian Tanner, Adam White:
RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments. J. Mach. Learn. Res. 10: 2133-2136 (2009) - 2006
- [c1]Nathan R. Sturtevant, Adam M. White:
Feature Construction for Reinforcement Learning in Hearts. Computers and Games 2006: 122-134
Coauthor Index
[j15] [j14] [j13] [j12] [c29] [i46] [i45] [i44] [i43] [i42] [i41] [j9] [c25] [c24] [c23] [i40] [i38] [i37] [i34] [j7] [j6] [i31] [i29] [i27] [i26] [j5] [c20] [i23] [j4] [c18] [c17] [i18] [i17] [c16] [c14] [i16] [i15] [i14] [c13] [c12] [c11] [i13] [i12] [i11] [i10] [i9] [i8] [c10] [c8] [c7] [i6] [i4] [i3] [c2]
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last updated on 2024-11-25 22:47 CET by the dblp team
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