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D2RL: Deep Dense Architectures in Reinforcement Learning
While improvements in deep learning architectures have played a crucial ...
10/19/2020 ∙ by Samarth Sinha, et al. ∙
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Evaluating Self-Supervised Pretraining Without Using Labels
A common practice in unsupervised representation learning is to use labe...
09/16/2020 ∙ by Colorado Reed, et al. ∙
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SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Model-free deep reinforcement learning (RL) has been successful in a ran...
07/09/2020 ∙ by Kimin Lee, et al. ∙
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Reinforcement Learning with Augmented Data
Learning from visual observations is a fundamental yet challenging probl...
04/30/2020 ∙ by Michael (Misha) Laskin, et al. ∙
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CURL: Contrastive Unsupervised Representations for Reinforcement Learning
We present CURL: Contrastive Unsupervised Representations for Reinforcem...
04/08/2020 ∙ by Aravind Srinivas, et al. ∙
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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Flow-based generative models are powerful exact likelihood models with e...
02/01/2019 ∙ by Jonathan Ho, et al. ∙
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Universal Planning Networks
A key challenge in complex visuomotor control is learning abstract repre...
04/02/2018 ∙ by Aravind Srinivas, et al. ∙
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