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10/25/2022
In-context Reinforcement Learning with Algorithm Distillation
We propose Algorithm Distillation (AD), a method for distilling reinforc...
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10/19/2022
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining
Large and diverse datasets have been the cornerstones of many impressive...
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10/28/2021
Wasserstein Distance Maximizing Intrinsic Control
This paper deals with the problem of learning a skill-conditioned policy...
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12/14/2020
Relative Variational Intrinsic Control
In the absence of external rewards, agents can still learn useful behavi...
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01/22/2020
Q-Learning in enormous action spaces via amortized approximate maximization
Applying Q-learning to high-dimensional or continuous action spaces can ...
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06/19/2019
Unsupervised Learning of Object Keypoints for Perception and Control
The study of object representations in computer vision has primarily foc...
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06/12/2019
Fast Task Inference with Variational Intrinsic Successor Features
It has been established that diverse behaviors spanning the controllable...
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11/28/2018
Unsupervised Control Through Non-Parametric Discriminative Rewards
Learning to control an environment without hand-crafted rewards or exper...
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02/28/2018
Learning by Playing - Solving Sparse Reward Tasks from Scratch
We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm ...
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09/15/2017
The Uncertainty Bellman Equation and Exploration
We consider the exploration/exploitation problem in reinforcement learni...
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11/16/2016
Reinforcement Learning with Unsupervised Auxiliary Tasks
Deep reinforcement learning agents have achieved state-of-the-art result...
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11/05/2016
Combining policy gradient and Q-learning
Policy gradient is an efficient technique for improving a policy in a re...
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10/20/2016
Using Fast Weights to Attend to the Recent Past
Until recently, research on artificial neural networks was largely restr...
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06/15/2016
Strategic Attentive Writer for Learning Macro-Actions
We present a novel deep recurrent neural network architecture that learn...
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02/24/2016
Learning values across many orders of magnitude
Most learning algorithms are not invariant to the scale of the function ...
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02/04/2016
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep rein...
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07/15/2015
Massively Parallel Methods for Deep Reinforcement Learning
We present the first massively distributed architecture for deep reinfor...
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12/24/2014
Multiple Object Recognition with Visual Attention
We present an attention-based model for recognizing multiple objects in ...
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06/24/2014
Recurrent Models of Visual Attention
Applying convolutional neural networks to large images is computationall...
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12/19/2013
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control p...
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02/14/2012
