Search Results for author: Danijar Hafner

Found 36 papers, 20 papers with code

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

2 code implementations NeurIPS 2018 Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity.

Continuous Control reinforcement-learning +1

Mastering Atari with Discrete World Models

8 code implementations ICLR 2021 Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba

The world model uses discrete representations and is trained separately from the policy.

Ranked #3 on Atari Games on Atari 2600 Skiing (using extra training data)

Atari Games

TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

2 code implementations8 Sep 2017 Danijar Hafner, James Davidson, Vincent Vanhoucke

We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow.

reinforcement-learning Reinforcement Learning (RL)

Benchmarking the Spectrum of Agent Capabilities

1 code implementation ICLR 2022 Danijar Hafner

We hope that Crafter will accelerate research progress by quickly evaluating a wide spectrum of abilities.

Benchmarking

DayDreamer: World Models for Physical Robot Learning

1 code implementation28 Jun 2022 Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel

Learning a world model to predict the outcomes of potential actions enables planning in imagination, reducing the amount of trial and error needed in the real environment.

Navigate reinforcement-learning +1

Planning to Explore via Self-Supervised World Models

4 code implementations12 May 2020 Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak

Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge.

reinforcement-learning Reinforcement Learning (RL)

Noise Contrastive Priors for Functional Uncertainty

2 code implementations ICLR 2019 Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training.

Active Learning

Evaluating Long-Term Memory in 3D Mazes

1 code implementation24 Oct 2022 Jurgis Pasukonis, Timothy Lillicrap, Danijar Hafner

However, most benchmark tasks in reinforcement learning do not test long-term memory in agents, slowing down progress in this important research direction.

Navigate reinforcement-learning +1

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

1 code implementation8 Dec 2020 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Harini Kannan, Chelsea Finn, Sergey Levine, Dumitru Erhan

In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.

Model-based Reinforcement Learning Reinforcement Learning (RL)

Temporally Consistent Transformers for Video Generation

1 code implementation5 Oct 2022 Wilson Yan, Danijar Hafner, Stephen James, Pieter Abbeel

To generate accurate videos, algorithms have to understand the spatial and temporal dependencies in the world.

Video Generation Video Prediction

Discovering and Achieving Goals via World Models

2 code implementations NeurIPS 2021 Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak

How can artificial agents learn to solve many diverse tasks in complex visual environments in the absence of any supervision?

Deep Hierarchical Planning from Pixels

1 code implementation8 Jun 2022 Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel

Despite operating in latent space, the decisions are interpretable because the world model can decode goals into images for visualization.

Atari Games Hierarchical Reinforcement Learning

Clockwork Variational Autoencoders

2 code implementations NeurIPS 2021 Vaibhav Saxena, Jimmy Ba, Danijar Hafner

We introduce the Clockwork VAE (CW-VAE), a video prediction model that leverages a hierarchy of latent sequences, where higher levels tick at slower intervals.

Video Prediction

Learning Robust Dynamics through Variational Sparse Gating

1 code implementation21 Oct 2022 Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi-Kahou

Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes.

Inductive Bias

Evaluating Agents without Rewards

1 code implementation21 Dec 2020 Brendon Matusch, Jimmy Ba, Danijar Hafner

Moreover, input entropy and information gain correlate more strongly with human similarity than task reward does, suggesting the use of intrinsic objectives for designing agents that behave similarly to human players.

Atari Games

Deep Reinforcement Learning From Raw Pixels in Doom

no code implementations7 Oct 2016 Danijar Hafner

Using current reinforcement learning methods, it has recently become possible to learn to play unknown 3D games from raw pixels.

reinforcement-learning Reinforcement Learning (RL)

Modulated Policy Hierarchies

no code implementations30 Nov 2018 Alexander Pashevich, Danijar Hafner, James Davidson, Rahul Sukthankar, Cordelia Schmid

To achieve this, we study different modulation signals and exploration for hierarchical controllers.

Reinforcement Learning (RL)

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

no code implementations ICLR 2019 Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

NCPs are compatible with any model that can output uncertainty estimates, are easy to scale, and yield reliable uncertainty estimates throughout training.

Active Learning

Sophisticated Inference

no code implementations7 Jun 2020 Karl Friston, Lancelot Da Costa, Danijar Hafner, Casper Hesp, Thomas Parr

In this paper, we consider a sophisticated kind of active inference, using a recursive form of expected free energy.

Active Learning counterfactual

Action and Perception as Divergence Minimization

1 code implementation3 Sep 2020 Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess

While the narrow objectives correspond to domain-specific rewards as typical in reinforcement learning, the general objectives maximize information with the environment through latent variable models of input sequences.

Decision Making Representation Learning

On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning

no code implementations1 Jan 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Dumitru Erhan, Harini Kannan, Chelsea Finn, Sergey Levine

In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.

Model-based Reinforcement Learning reinforcement-learning +1

Video Prediction with Variational Temporal Hierarchies

no code implementations1 Jan 2021 Vaibhav Saxena, Jimmy Ba, Danijar Hafner

Deep learning has shown promise for accurately predicting high-dimensional video sequences.

Video Prediction

Latent Skill Planning for Exploration and Transfer

no code implementations ICLR 2021 Kevin Xie, Homanga Bharadhwaj, Danijar Hafner, Animesh Garg, Florian Shkurti

To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge.

Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments

no code implementations ICML Workshop URL 2021 Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine

We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.

Discovering and Achieving Goals with World Models

no code implementations ICML Workshop URL 2021 Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, Deepak Pathak

How can an artificial agent learn to solve a wide range of tasks in a complex visual environment in the absence of external supervision?

Information is Power: Intrinsic Control via Information Capture

no code implementations NeurIPS 2021 Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine

We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.

Masked World Models for Visual Control

no code implementations28 Jun 2022 Younggyo Seo, Danijar Hafner, Hao liu, Fangchen Liu, Stephen James, Kimin Lee, Pieter Abbeel

Yet the current approaches typically train a single model end-to-end for learning both visual representations and dynamics, making it difficult to accurately model the interaction between robots and small objects.

Model-based Reinforcement Learning Reinforcement Learning (RL) +1

Learning to Model the World with Language

no code implementations31 Jul 2023 Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, Anca Dragan

To interact with humans in the world, agents need to understand the diverse types of language that people use, relate them to the visual world, and act based on them.

Future prediction General Knowledge +1

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