I am a PhD student at the University of Toronto, advised by Jimmy Ba. I am also a student researcher at Google Brain in Geoffrey Hinton’s team. I finised my MRes in CSML at University College London, advised by Tim Lillicrap and Karl Friston.

My goal is to build intelligent machines based on concepts of the human brain, and evaluate them in complex simulations. Currently, I’m focusing on agents that learn without rewards, model-based reinforcement learning, and variational inference.

If you have any questions, feel free to contact me at [email protected].

To work with me at Vector Institute, please fill out the application form.

News

2018-11-28 We organize the ICLR 2019 workshop on task-agnostic RL.

2018-09-05 STEVE paper accepted for oral presentation at NIPS 2018.

2018-04-12 Sim-to-real Minitaur paper accepted at RSS 2018.

2018-02-16 Guest lecture at Stanford CS 20 on Variational Inference in TF.

2018-02-14 Talk at IBM Research AI on ThalNet.

Selected research

Please see my Scholar profile for a full list including article links.

• 
  Dec 3, 2018

  Learning Latent Dynamics for Planning from Pixels

  D Hafner, T Lillicrap, I Fischer, R Villegas, D Ha, H Lee, J Davidson
• 
  Nov 30, 2018  (NeurIPS 2018 DRL Workshop)

  Modulated Policy Hierarchies

  A Pashevich, D Hafner, J Davidson, R Sukthankar, C Schmid
• 
  Jul 14, 2018  (NeurIPS 2018, oral 0.6%)

  Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

  J Buckman, D Hafner, G Tucker, E Brevdo, H Lee
• 
  Jul 14, 2018  (TADGM 2018)

  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

  D Hafner, D Tran, A Irpan, T Lillicrap, J Davidson
• 
  Apr 27, 2018  (RSS 2018)

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

  J Tan, T Zhang, E Coumans, A Iscen, Y Bai, D Hafner, S Bohez, V Vanhoucke
• 
  Nov 8, 2017  (Tech report)

  TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

  D Hafner, J Davidson, V Vanhoucke
• 
  Jul 18, 2017  (NIPS 2017, 21%)

  Learning Hierarchical Information Flow with Recurrent Neural Modules

  D Hafner, A Irpan, J Davidson, N Heess
• 
  Aug 22, 2016  (ESA 2016, 27%)

  Probabilistic Routing for On-Street Parking Search

  T Arndt, D Hafner, T Kellermeier, S Krogmann, A Razmjou, M Krejca, R Rothenberger, T Friedrich
• 
  Jul 20, 2016  (Tech report)

  Generative Interest Estimation for Document Recommendations

  D Hafner, A Immer, W Raschkowski, F Windheuser
• 
  Jul 15, 2016  (BSc thesis)

  Deep Reinforcement Learning From Raw Pixels in Doom

  D Hafner

Short biography

Danijar Hafner is a PhD candidate at the University of Toronto under the supervision of Jimmy Ba, where he focuses on designing agents that learn without rewards. Danijar obtained his MRes at the University College London and Gatsby Unit under the supervision of Timothy Lillicrap and Karl Friston. He is also a student researcher at Google Brain. Danijar co-authored the book “TensorFlow for Machine Intelligence” and advises Stanford’s course “TensorFlow for Deep Learning Research”. He completed his bachelor’s thesis on deep reinforcement learning for the video game Doom at the Hasso Plattner Institute, Germany.

Links

Twitter · Github · Quora · LinkedIn · Google AI · ORCID