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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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| Vydáno v: | PLoS One |
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| Hlavní autoři: | , , |
| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
Public Library of Science
2019
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| Témata: | |
| On-line přístup: | 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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