Danijar Hafner

research

∙ 07/31/2023

Learning to Model the World with Language

To interact with humans in the world, agents need to understand the dive...

0 Jessy Lin, et al. ∙

research

∙ 05/23/2023

Video Prediction Models as Rewards for Reinforcement Learning

Specifying reward signals that allow agents to learn complex behaviors i...

4 Alejandro Escontrela, et al. ∙

research

∙ 01/10/2023

Mastering Diverse Domains through World Models

General intelligence requires solving tasks across many domains. Current...

0 Danijar Hafner, et al. ∙

research

∙ 10/24/2022

Evaluating Long-Term Memory in 3D Mazes

Intelligent agents need to remember salient information to reason in par...

0 Jurgis Pašukonis, et al. ∙

research

∙ 10/05/2022

Temporally Consistent Video Transformer for Long-Term Video Prediction

Generating long, temporally consistent video remains an open challenge i...

0 Wilson Yan, et al. ∙

research

∙ 06/28/2022

Masked World Models for Visual Control

Visual model-based reinforcement learning (RL) has the potential to enab...

0 Younggyo Seo, et al. ∙

research

∙ 06/28/2022

DayDreamer: World Models for Physical Robot Learning

To solve tasks in complex environments, robots need to learn from experi...

10 Philipp Wu, et al. ∙

research

∙ 06/08/2022

Deep Hierarchical Planning from Pixels

Intelligent agents need to select long sequences of actions to solve com...

0 Danijar Hafner, et al. ∙

research

∙ 12/07/2021

Information is Power: Intrinsic Control via Information Capture

Humans and animals explore their environment and acquire useful skills e...

13 Nicholas Rhinehart, et al. ∙

research

∙ 10/18/2021

Discovering and Achieving Goals via World Models

How can artificial agents learn to solve many diverse tasks in complex v...

8 Russell Mendonca, et al. ∙

research

∙ 09/14/2021

Benchmarking the Spectrum of Agent Capabilities

Evaluating the general abilities of intelligent agents requires complex ...

1 Danijar Hafner, et al. ∙

research

∙ 02/18/2021

Clockwork Variational Autoencoders

Deep learning has enabled algorithms to generate realistic images. Howev...

0 Vaibhav Saxena, et al. ∙

research

∙ 12/21/2020

Evaluating Agents without Rewards

Reinforcement learning has enabled agents to solve challenging tasks in ...

17 Brendon Matusch, et al. ∙

research

∙ 12/08/2020

Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) methods have shown strong samp...

7 Mohammad Babaeizadeh, et al. ∙

research

∙ 11/27/2020

Skill Transfer via Partially Amortized Hierarchical Planning

To quickly solve new tasks in complex environments, intelligent agents n...

0 Kevin Xie, et al. ∙

research

∙ 10/05/2020

Mastering Atari with Discrete World Models

Intelligent agents need to generalize from past experience to achieve go...

17 Danijar Hafner, et al. ∙

research

∙ 09/03/2020

Action and Perception as Divergence Minimization

We introduce a unified objective for action and perception of intelligen...

10 Danijar Hafner, et al. ∙

research

∙ 06/07/2020

Sophisticated Inference

Active inference offers a first principle account of sentient behaviour,...

27 Karl Friston, et al. ∙

research

∙ 05/12/2020

Planning to Explore via Self-Supervised World Models

Reinforcement learning allows solving complex tasks, however, the learni...

2 Ramanan Sekar, et al. ∙

research

∙ 12/03/2019

Dream to Control: Learning Behaviors by Latent Imagination

Learned world models summarize an agent's experience to facilitate learn...

0 Danijar Hafner, et al. ∙

research

∙ 12/10/2018

Bayesian Layers: A Module for Neural Network Uncertainty

We describe Bayesian Layers, a module designed for fast experimentation ...

14 Dustin Tran, et al. ∙

research

∙ 11/30/2018

Modulated Policy Hierarchies

Solving tasks with sparse rewards is a main challenge in reinforcement l...

0 Alexander Pashevich, et al. ∙

research

∙ 11/12/2018

Learning Latent Dynamics for Planning from Pixels

Planning has been very successful for control tasks with known environme...

6 Danijar Hafner, et al. ∙

research

∙ 07/24/2018

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

Obtaining reliable uncertainty estimates of neural network predictions i...

8 Danijar Hafner, et al. ∙

research

∙ 07/04/2018

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Integrating model-free and model-based approaches in reinforcement learn...

6 Jacob Buckman, et al. ∙

research

∙ 04/27/2018

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

Designing agile locomotion for quadruped robots often requires extensive...

0 Jie Tan, et al. ∙

research

∙ 11/28/2017

Generative Interest Estimation for Document Recommendations

Learning distributed representations of documents has pushed the state-o...

0 Danijar Hafner, et al. ∙

research

∙ 09/08/2017

TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

We introduce TensorFlow Agents, an efficient infrastructure paradigm for...

0 Danijar Hafner, et al. ∙

research

∙ 06/18/2017

Learning Hierarchical Information Flow with Recurrent Neural Modules

We propose ThalNet, a deep learning model inspired by neocortical commun...

0 Danijar Hafner, et al. ∙

research

∙ 10/07/2016

Deep Reinforcement Learning From Raw Pixels in Doom

Using current reinforcement learning methods, it has recently become pos...

0 Danijar Hafner, et al. ∙

Danijar Hafner

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