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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… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient… Expand
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Highly Cited
2016
Highly Cited
2016
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known… Expand
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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… Expand
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Highly Cited
2008
Highly Cited
2008
Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This… Expand
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Highly Cited
2005
Highly Cited
2005
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning… Expand
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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… Expand
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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… Expand
Highly Cited
2000
Highly Cited
2000
Objective—To evaluate the pharmacokinetics of a novel commercial formulation of ivermectin after administration to goats. Animals… Expand
Highly Cited
1998
Highly Cited
1998
From the Publisher: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key… Expand
Review
1996
Review
1996
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to… Expand
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