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Maximizing Local Rewards on Multi-Agent Quantum Games through Gradient-Based Learning Strategies

This article delves into the complex world of quantum games in multi-agent settings, proposing a model wherein agents utilize gradient-based strategies to optimize local rewards. A learning model is introduced to focus on the learning efficacy of agents in various games and the impact of quantum cir...

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Detalles Bibliográficos
Autores principales:	Silva, Agustin, Zabaleta, Omar Gustavo, Arizmendi, Constancio Miguel
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670538/ https://www.ncbi.nlm.nih.gov/pubmed/37998177 http://dx.doi.org/10.3390/e25111484

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