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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… 
Wikipedia

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… 
Highly Cited
2016
Highly Cited
2016
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model… 
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… 
Highly Cited
2013
Highly Cited
2013
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input… 
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… 
Highly Cited
2005
Highly Cited
2005
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning… 
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… 
Highly Cited
2004
Highly Cited
2004
This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing… 
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… 
Review
1996
Review
1996
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to…