Andrew Barto

Known as: Andrew G. Barto, Barto, Barto, Andrew G. 
Andrew Barto is a professor of computer science at University of Massachusetts Amherst, and chair of the department since January 2007. His main… (More)
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Papers overview

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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… (More)
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Highly Cited
2012
Highly Cited
2012
We describe CST, an online algorithm for constructing skill trees from demonstration trajectories. CST segments a demonstration… (More)
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Highly Cited
2010
Highly Cited
2010
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of… (More)
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Highly Cited
2007
Highly Cited
2007
The options framework provides a method for reinforcement learning agents to build new high-level skills. However, since options… (More)
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2005
2005
One of the primary challenges of developmental robotics is the question of how to learn and represent increasingly complex… (More)
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Highly Cited
2000
Highly Cited
2000
Temporal-di erence (TD) learning can be used not just to predict rewards, as is commonly done in reinforcement learning, but also… (More)
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Highly Cited
1999
Highly Cited
1999
Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and… (More)
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Highly Cited
1992
Highly Cited
1992
Internal models of the environment have an important role to play in adaptive systems in general and are of particular importance… (More)
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Highly Cited
1990
Highly Cited
1990
This chapter presents a model of classical conditioning called the temporal-diierence (TD) model. The TD model was originally… (More)
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