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Exploratory combinatorial optimization with reinforcement learning

Abstract:

Many real-world problems can be reduced to combinatorial optimization on a graph, where the subset or ordering of vertices that maximize some objective function must be found. With such tasks often NP-hard and analytically intractable, reinforcement learning (RL) has shown promise as a framework with which efficient heuristic methods to tackle these problems can be learned. Previous works construct the solution subset incrementally, adding one element at a time, however, the irreversible natu...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1609/aaai.v34i04.5723

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Physics
Subgroup:
Atomic & Laser Physics
Role:
Depositor, Author
ORCID:
0000-0001-6241-3028
Clements, WR More by this author
Foerster, JN More by this author
More by this author
Institution:
University of Oxford
Department:
Physics
Subgroup:
Atomic & Laser Physics
Publisher:
Association for the Advancement of Artificial Intelligence Publisher's website
Volume:
34
Issue:
4
Host title:
Proceedings of the AAAI Conference on Artificial Intelligence
Publication date:
2020-06-16
Acceptance date:
2019-11-10
Event title:
Thirty-Fourth AAAI Conference on Artificial Intelligence
Event location:
New York
Event website:
https://aaai.org/Conferences/AAAI-20/
Event start date:
2020-02-07T00:00:00Z
Event end date:
2020-02-12T00:00:00Z
DOI:
Pubs id:
pubs:1053693
UUID:
uuid:221b3c94-e9c1-40c3-934a-236f259741d0
Source identifiers:
1053693
Local pid:
pubs:1053693
Language:
English
Keywords:

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