Projects

Action and Perception as Divergence Minimization

D Hafner, P Ortega, J Ba, T Parr, K Friston, N Heess

Planning to Explore via Self-Supervised World Models

R Sekar, O Rybkin, K Daniilidis, P Abbeel, D Hafner, D Pathak

Dream to Control: Learning Behaviors by Latent Imagination

D Hafner, T Lillicrap, J Ba, M Norouzi

A Deep Learning Framework for Neuroscience

Richards, Lillicrap, Beaudoi, Bengio, Bogacz, Christensen, Clopath, Costa, Berker, Ganguli, Gillon, Hafner, Kepecs, Kriegeskorte, Latham, Lindsay, Miller, Naud, Pack, Poirazi, Roelfsema, Sacramento, Saxe, Scellier, Schapiro, Senn, Wayne, Yamins, Zenke, Zylberberg, Therien, Kording

Noise Contrastive Priors for Functional Uncertainty

D Hafner, D Tran, A Irpan, T Lillicrap, J Davidson

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

J Buckman, D Hafner, G Tucker, E Brevdo, H Lee

Learning Latent Dynamics for Planning from Pixels

D Hafner, T Lillicrap, I Fischer, R Villegas, D Ha, H Lee, J Davidson

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

TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

D Hafner, J Davidson, V Vanhoucke

Learning Hierarchical Information Flow with Recurrent Neural Modules

D Hafner, A Irpan, J Davidson, N Heess

Deep Reinforcement Learning From Raw Pixels in Doom

D Hafner