Hi, I am a PhD
student in artificial intelligence at the University of Toronto with
Jimmy Ba and Geoffrey Hinton and a researcher at Google
Brain. I’m currently visiting Pieter Abbeel’s lab at UC
Berkeley. Previously, I completed my MRes in Computational Statistics
and Machine Learning at UCL and the Gatsby Unit with Tim
Lillicrap and Karl Friston. My work is supported by Canada’s
Vanier Scholarship.
Preferred way of being contacted: [email protected]
Research
The goal of my research is to discover the computational principles of human thinking by building intelligent machines that learn through interaction with complex environments. Within deep reinforcement learning, my work focuses on:
- World models learned from raw video data for artificial intelligence to develop a general understanding of the world and plan by imagining future outcomes of actions. The main challenges here are representation learning and temporal abstraction.
- Intrinsic motivation to autonomously explore and influence the environment, moving artificial intelligence beyond narrow task-specified behaviors. This includes artificial curiosity, information gain, empowerment, skill discovery, and active inference.
You can learn more on my research page.
Highlighted Work
See Google Scholar for more publications.
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Discovering and Achieving Goals via World Models
NeurIPS 2021 (26%), URL 2021 (oral), SSL 2021 (oral)
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Benchmarking the Spectrum of Agent Capabilities
ICLR 2022, DRLW 2021 (oral)
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Clockwork Variational Autoencoders
NeurIPS 2021 (26%)
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Evaluating Agents without Rewards
BARL 2020 (oral)
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Latent Skill Planning for Exploration and Transfer
ICLR 2021 (28%)
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Mastering Atari with Discrete World Models
ICLR 2021 (28%)
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Planning to Explore via Self-Supervised World Models
ICML 2020 (22%)
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Dream to Control: Learning Behaviors by Latent Imagination
ICLR 2020 (oral, 4%), DRLW 2019 (oral)
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A Deep Learning Framework for Neuroscience
Nature Neuroscience
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Noise Contrastive Priors for Functional Uncertainty
UAI 2019 (26%)
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Learning Latent Dynamics for Planning from Pixels
ICML 2019 (23%)
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Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
NeurIPS 2018 (oral, 0.6%)
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Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
RSS 2018 (31%)
Short Bio
Danijar Hafner is a visiting student at the University California, Berkeley with Pieter Abbeel and a PhD candidate in artificial intelligence at the University of Toronto with Jimmy Ba and Geoffrey Hinton. He is also a student researcher at Google Research and the Vector Institute. Danijar’s research aims to build intelligent machines based on the computational principles of the brain. Towards this goal, he focuses on scaling up embodied artificial intelligence through general world models, unsupervised objectives, and deep reinforcement learning. Danijar completed his MRes in Computational Statistics and Machine Learning at UCL and the Gatsby Unit with Tim Lillicrap and Karl Friston. His work is supported by Canada’s Vanier Scholarship.