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Multi-agent reinforcement learning with approximate model learning for competitive games

We propose a method for learning multi-agent policies to compete against multiple opponents. The method consists of recurrent neural network-based actor-critic networks and deterministic policy gradients that promote cooperation between agents by communication. The learning process does not require...

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Pubblicato in:PLoS One
Autori principali: Park, Young Joon, Cho, Yoon Sang, Kim, Seoung Bum
Natura: Artigo
Lingua:Inglês
Pubblicazione: Public Library of Science 2019
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Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC6739057/
https://ncbi.nlm.nih.gov/pubmed/31509568
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0222215
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