Dabney, Will

Rainbow: Combining Improvements in Deep Reinforcement Learning

Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., & Silver, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1,846

Distributional Reinforcement Learning With Quantile Regression

Dabney, W., Rowland, M., Bellemare, M., & Munos, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 612

Implicit Quantile Networks for Distributional Reinforcement Learning

Dabney, W., Ostrovski, G., Silver, D., & Munos, R. (2018) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 331

Deep reinforcement learning and its neuroscientific implications

Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 269

The Cramer Distance as a Solution to Biased Wasserstein Gradients

Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., & Munos, R. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisGeometric Analysis and Curvature Flows

Cited by 223

The Difficulty of Passive Learning in Deep Reinforcement Learning

Ostrovski, G., Castro, P. S., & Dabney, W. (2021) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 15

Understanding and Preventing Capacity Loss in Reinforcement Learning

Lyle, C., Rowland, M., & Dabney, W. (2022) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 13

Understanding plasticity in neural networks

Lyle, C., Zheng, Z., Nikishin, E., Pires, B. A., Pascanu, R., & Dabney, W. (2023) · arXiv

Reinforcement Learning in RoboticsNeural Networks and Applications

Cited by 11

Disentangling the Causes of Plasticity Loss in Neural Networks

Lyle, C., Zheng, Z., Khetarpal, K., van Hasselt, H., Pascanu, R., Martens, J., & Dabney, W. (2024) · arXiv

Neural Networks and ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 5

Generalised Policy Improvement with Geometric Policy Composition

Thakoor, S., Rowland, M., Borsa, D., Dabney, W., Munos, R., & Barreto, A. (2022) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 5

Settling the Reward Hypothesis

Bowling, M., Martin, J. D., Abel, D., & Dabney, W. (2022) · arXiv

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 4

Deep Reinforcement Learning with Plasticity Injection

Nikishin, E., Oh, J., Ostrovski, G., Lyle, C., Pascanu, R., Dabney, W., & Barreto, A. (2023) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 4

Agency Is Frame-Dependent

Abel, D., Barreto, A., Bowling, M., Dabney, W., Dong, S., Hansen, S., Harutyunyan, A., Khetarpal, K., Lyle, C., Pascanu, R., et al. (2025) · arXiv

Philosophy of Biology and Scientific ExplanationEmbodied and Extended Cognition

Cited by 0

Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability

Kuo, P.-C., Hou, H., Dabney, W., & Walker, E. (2025) · Advances in Neural Information Processing Systems 38

Reinforcement Learning in RoboticsBayesian Modeling and Causal Inference

Cited by 0

Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability

Kuo, P.-C., Hou, H., Dabney, W., & Walker, E. Y. (2025) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 0