Search Results for author: Danijar Hafner

Found 28 papers, 14 papers with code

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.

Discovering and Achieving Goals via World Models

1 code implementation 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?

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.

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?

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.

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

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

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

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

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

Mastering Atari with Discrete World Models

6 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

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

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

Planning to Explore via Self-Supervised World Models

3 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

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

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

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

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

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

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

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