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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739057/ https://www.ncbi.nlm.nih.gov/pubmed/31509568 http://dx.doi.org/10.1371/journal.pone.0222215 |