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Cited by 2,769

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Apprenticeship learning via inverse reinforcement learning

Abbeel, Pieter, Ng, Andrew Y.·2004·Twenty-first international conference on Machine learning - ICML '04

Abstract

We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be difficult to write down an explicit reward function specifying exactly how different desiderata should be traded off. We think of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and give an algorithm for learning the task demonstrated by the expert. Our algorithm is based on using "inverse reinforcement learning" to try to recover the unknown reward function. We show that our algorithm terminates in a small number of iterations, and that even though we may never recover the expert's reward function, the policy output by the algorithm will attain performance close to that of the expert, where here performance is measured with respect to the expert's unknown reward function.

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Metadata

Authors
Abbeel, P., & Ng, A. Y.
Venue
Twenty-first international conference on Machine learning - ICML '04
Publisher
ACM Press
Published
2004
Pages
1
OpenAlex
W1999874108
Cited by
2,769
References
13
Type
conference abstract
Language
en
Open access
closed

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CoChat. (2026). Citation check: Apprenticeship learning via inverse reinforcement learning. https://app.cochat.ai/papers/10.1145/1015330.1015430
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Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. Twenty-first international conference on Machine learning - ICML '04. https://doi.org/10.1145/1015330.1015430

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