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Alternating optimisation and quadrature for robust control

Abstract:

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and randomly determined by the environment in a physical setting but are controllable in a simulator. This paper considers the problem of finding a robust policy while taking into acco...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Name:
European Research Council
Grant:
637713
637972
Publisher:
AAAI Press
Host title:
32nd AAAI Conference on Artificial Intelligence (AAAI'18)
Journal:
32nd AAAI Conference on Artificial Intelligence (AAAI'18) More from this journal
Pages:
3925-3933
Publication date:
2018-04-29
Acceptance date:
2017-11-09
ISSN:
2159-5399
Keywords:
Pubs id:
pubs:745008
UUID:
uuid:abd7c997-b0fb-4e66-b601-f82184500cbf
Local pid:
pubs:745008
Source identifiers:
745008
Deposit date:
2017-11-11

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