Hafner, Danijar

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 1,027

Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · Robotics: Science and Systems XIV

Robotic Locomotion and ControlReinforcement Learning in Robotics

Cited by 735

Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., & Vanhoucke, V. (2018) · arXiv

Robotic Locomotion and ControlReinforcement Learning in Robotics

Cited by 734

Learning Latent Dynamics for Planning from Pixels

Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2018) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 708

Dream to Control: Learning Behaviors by Latent Imagination

Hafner, D., Lillicrap, T., Ba, J., & Norouzi, M. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 558

Mastering Atari with Discrete World Models

Hafner, D., Lillicrap, T., Norouzi, M., & Ba, J. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 247

Mastering diverse control tasks through world models

Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2025) · Nature

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 189

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Buckman, J., Hafner, D., Tucker, G., Brevdo, E., & Lee, H. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 161

Planning to Explore via Self-Supervised World Models

Sekar, R., Rybkin, O., Daniilidis, K., Abbeel, P., Hafner, D., & Pathak, D. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 129

Mastering Diverse Domains through World Models

Hafner, D., Pasukonis, J., Ba, J., & Lillicrap, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 124

DayDreamer: World Models for Physical Robot Learning

Wu, P., Escontrela, A., Hafner, D., Goldberg, K., & Abbeel, P. (2022) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 76

Benchmarking the Spectrum of Agent Capabilities

Hafner, D. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 16

One Step Diffusion via Shortcut Models

Frans, K., Hafner, D., Levine, S., & Abbeel, P. (2024) · arXiv

Generative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods

Cited by 3

Evaluating Long-Term Memory in 3D Mazes

Pasukonis, J., Lillicrap, T., & Hafner, D. (2022) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3