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QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning

Abstract:

In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted. Learning joint actionvalues conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralis...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's Version

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Samvelyan, M More by this author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
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Innovation Fund Denmark More from this funder
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Publisher:
Journal of Machine Learning Research Publisher's website
Publication date:
2018-07-03
Acceptance date:
2018-06-12
Pubs id:
pubs:857023
URN:
uri:4e16ec00-f9e2-48ef-83fe-92e2b845fb87
UUID:
uuid:4e16ec00-f9e2-48ef-83fe-92e2b845fb87
Local pid:
pubs:857023

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