Apprenticeship learning

Apprenticeship learning, or apprenticeship via inverse reinforcement learning (AIRP), is a concept in the field of artificial intelligence and… (More)
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Highly Cited
2011
Highly Cited
2011
In this paper, we apply tools from inverse reinforcement learning (IRL) to the problem of learning from (unlabeled) demonstration… (More)
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Highly Cited
2010
Highly Cited
2010
Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can… (More)
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Highly Cited
2008
Highly Cited
2008
In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy… (More)
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Highly Cited
2007
Highly Cited
2007
We study the problem of an apprentice learning to behave in an environment with an unknown reward function by observing the… (More)
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Highly Cited
2007
Highly Cited
2007
We consider apprenticeship learning—learning from expert demonstrations—in the setting of large, complex domains. Past work in… (More)
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Highly Cited
2007
Highly Cited
2007
In this paper we propose a novel gradient algorithm to learn a policy from an expert’s observed behavior assuming that the expert… (More)
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Highly Cited
2007
Highly Cited
2007
Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given… (More)
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Highly Cited
2007
Highly Cited
2007
Noting that skills and knowledge taught in schools have become abstracted from their uses in the world, this paper clarifies some… (More)
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Highly Cited
2005
Highly Cited
2005
We consider reinforcement learning in systems with unknown dynamics. Algorithms such as E3 (Kearns and Singh, 2002) learn near… (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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