
On the modelbased stochastic value gradient for continuous reinforcement learning
Modelbased reinforcement learning approaches add explicit domain knowle...
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Automatic Data Augmentation for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
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Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
We propose a simple data augmentation technique that can be applied to s...
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On the adequacy of untuned warmup for adaptive optimization
Adaptive optimization algorithms such as Adam (Kingma Ba, 2014) are ...
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Generalized Inner Loop MetaLearning
Many (but not all) approaches selfqualifying as "metalearning" in deep...
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Improving Sample Efficiency in ModelFree Reinforcement Learning from Images
Training an agent to solve control tasks directly from highdimensional ...
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The Differentiable CrossEntropy Method
We study the CrossEntropy Method (CEM) for the nonconvex optimization ...
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Hierarchical Decision Making by Generating and Following Natural Language Instructions
We explore using latent natural language instructions as an expressive a...
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Quasihyperbolic momentum and Adam for deep learning
Momentumbased acceleration of stochastic gradient descent (SGD) is wide...
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Hierarchical Text Generation and Planning for Strategic Dialogue
Endtoend models for strategic dialogue are challenging to train, becau...
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Deal or No Deal? EndtoEnd Learning for Negotiation Dialogues
Much of human dialogue occurs in semicooperative settings, where agents...
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Convolutional Sequence to Sequence Learning
The prevalent approach to sequence to sequence learning maps an input se...
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Denis Yarats
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