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Multi-agent learning via gradient ascent activity-based credit assignment

We consider the situation in which cooperating agents learn to achieve a common goal based solely on a global return that results from all agents’ behavior. The method proposed is based on taking into account the agents’ activity, which can be any additional information to help solving multi-agent d...

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Detalles Bibliográficos
Autores principales: Sabri, Oussama, Lehéricy, Luc, Muzy, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502029/
https://www.ncbi.nlm.nih.gov/pubmed/37709830
http://dx.doi.org/10.1038/s41598-023-42448-9
Descripción
Sumario:We consider the situation in which cooperating agents learn to achieve a common goal based solely on a global return that results from all agents’ behavior. The method proposed is based on taking into account the agents’ activity, which can be any additional information to help solving multi-agent decentralized learning problems. We propose a gradient ascent algorithm and assess its performance on synthetic data.