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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

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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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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
35th International Conference on Machine Learning (ICML 2018) Journal website
Host title:
35th International Conference on Machine Learning (ICML 2018)
Publication date:
2018-07-03
Acceptance date:
2018-06-12
Source identifiers:
857023
Pubs id:
pubs:857023
UUID:
uuid:4e16ec00-f9e2-48ef-83fe-92e2b845fb87
Local pid:
pubs:857023
Deposit date:
2018-06-12

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