Konrad Zolna | DeepAI

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∙ 08/07/2023

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

StarCraft II is one of the most challenging simulated reinforcement lear...

0 Michael Mathieu, et al. ∙

∙ 06/20/2023

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation

The ability to leverage heterogeneous robotic experience from different ...

0 Konstantinos Bousmalis, et al. ∙

∙ 05/12/2022

A Generalist Agent

Inspired by progress in large-scale language modeling, we apply a simila...

12 Scott Reed, et al. ∙

∙ 06/28/2021

Robust Learning-Augmented Caching: An Experimental Study

Effective caching is crucial for the performance of modern-day computing...

0 Jakub Chłędowski, et al. ∙

∙ 03/17/2021

Regularized Behavior Value Estimation

Offline reinforcement learning restricts the learning process to rely on...

8 Caglar Gulcehre, et al. ∙

∙ 12/12/2020

Semi-supervised reward learning for offline reinforcement learning

In offline reinforcement learning (RL) agents are trained using a logged...

12 Ksenia Konyushkova, et al. ∙

∙ 11/27/2020

Offline Learning from Demonstrations and Unlabeled Experience

Behavior cloning (BC) is often practical for robot learning because it a...

6 Konrad Zolna, et al. ∙

∙ 07/17/2020

Hyperparameter Selection for Offline Reinforcement Learning

Offline reinforcement learning (RL purely from logged data) is an import...

30 Tom Le Paine, et al. ∙

∙ 06/26/2020

Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...

32 Ziyu Wang, et al. ∙

∙ 06/24/2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have the potential to help br...

10 Caglar Gulcehre, et al. ∙

∙ 02/02/2020

Combating False Negatives in Adversarial Imitation Learning

In adversarial imitation learning, a discriminator is trained to differe...

5 Konrad Zolna, et al. ∙

∙ 10/02/2019

Task-Relevant Adversarial Imitation Learning

We show that a critical problem in adversarial imitation from high-dimen...

35 Konrad Zolna, et al. ∙

∙ 09/26/2019

A Framework for Data-Driven Robotics

We present a framework for data-driven robotics that makes use of a larg...

0 Serkan Cabi, et al. ∙

∙ 06/12/2019

The Dynamics of Handwriting Improves the Automated Diagnosis of Dysgraphia

Handwriting disorder (termed dysgraphia) is a far from a singular proble...

0 Konrad Zolna, et al. ∙

∙ 04/06/2019

Split Batch Normalization: Improving Semi-Supervised Learning under Domain Shift

Recent work has shown that using unlabeled data in semi-supervised learn...

0 Michał Zając, et al. ∙

∙ 04/06/2019

Reinforced Imitation in Heterogeneous Action Space

Imitation learning is an effective alternative approach to learn a polic...

14 Konrad Zolna, et al. ∙

∙ 12/11/2018

Adversarial Framing for Image and Video Classification

Neural networks are prone to adversarial attacks. In general, such attac...

0 Michał Zając, et al. ∙

∙ 06/12/2018

Focused Hierarchical RNNs for Conditional Sequence Processing

Recurrent Neural Networks (RNNs) with attention mechanisms have obtained...

0 Nan Rosemary Ke, et al. ∙

∙ 05/21/2018

Classifier-agnostic saliency map extraction

We argue for the importance of decoupling saliency map extraction from a...

0 Konrad Zolna, et al. ∙

∙ 10/31/2017

Fraternal Dropout

Recurrent neural networks (RNNs) are important class of architectures am...

0 Konrad Zolna, et al. ∙

∙ 12/05/2016

Improving the Performance of Neural Networks in Regression Tasks Using Drawering

The method presented extends a given regression neural network to make i...

0 Konrad Zolna, et al. ∙

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