Reinforcement learning

Known as: RL, Actor critic architecture, Reward function 
Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in… (More)
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Papers overview

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Review
2017
Review
2017
This talk will present an overview of our recent research on distributional reinforcement learning. Our starting point is our… (More)
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Review
2017
Review
2017
We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six… (More)
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Highly Cited
2016
Highly Cited
2016
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient… (More)
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Highly Cited
2015
Highly Cited
2015
The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific… (More)
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Highly Cited
2015
Highly Cited
2015
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model… (More)
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Highly Cited
2013
Highly Cited
2013
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input… (More)
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Highly Cited
2005
Highly Cited
2005
Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered… (More)
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Highly Cited
2004
Highly Cited
2004
This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing… (More)
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Highly Cited
2004
Highly Cited
2004
We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can… (More)
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Highly Cited
1997
Highly Cited
1997
We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by… (More)
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