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Tuning mixed input hyperparameters on the fly for efficient population based AutoRL

Abstract:

Despite a series of recent successes in reinforcement learning (RL), many RL algorithms remain sensitive to hyperparameters. As such, there has recently been interest in the field of AutoRL, which seeks to automate design decisions to create more general algorithms. Recent work suggests that population based approaches may be effective AutoRL algorithms, by learning hyperparameter schedules on the fly. In particular, the PB2 algorithm is able to achieve strong performance in RL tasks by formu...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-9305-9268
Publisher:
Curran Associates Publisher's website
Host title:
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
Volume:
19
Pages:
15513-15528
Publication date:
2022-05-01
Acceptance date:
2021-09-28
Event title:
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Event location:
Virtual event
Event website:
https://nips.cc/Conferences/2021/
Event start date:
2021-12-06
Event end date:
2021-12-14
ISSN:
1049-5258
ISBN:
9781713845393
Language:
English
Keywords:
Pubs id:
1265403
Local pid:
pubs:1265403
Deposit date:
2023-01-20

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