Artículo
Learning Mixed Strategies in Quantum Games with Imperfect Information
Fecha de publicación:
10/2022
Editorial:
MDPI
Revista:
Quantum Reports
ISSN:
2624-960X
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
The quantization of games expand the players strategy space, allowing the emergence of more equilibriums. However, finding these equilibriums is difficult, especially if players are allowed to use mixed strategies. The size of the exploration space expands so much for quantum games that makes far harder to find the player’s best strategy. In this work, we propose a method to learn and visualize mixed quantum strategies and compare them with their classical counterpart. In our model, players do not know in advance which game they are playing (pay-off matrix) neither the action selected nor the reward obtained by their competitors at each step, they only learn from an individual feedback reward signal. In addition, we study both the influence of entanglement and noise on the performance of various quantum games.
Palabras clave:
GAME THEORY
,
MACHINE LEARNING
,
QUANTUM COMPUTING
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Articulos(ICYTE)
Articulos de INSTITUTO DE INVESTIGACIONES CIENTIFICAS Y TECNOLOGICAS EN ELECTRONICA
Articulos de INSTITUTO DE INVESTIGACIONES CIENTIFICAS Y TECNOLOGICAS EN ELECTRONICA
Citación
Silva, Agustin; Zabaleta, Omar Gustavo; Arizmendi, Constancio Miguel; Learning Mixed Strategies in Quantum Games with Imperfect Information; MDPI; Quantum Reports; 4; 4; 10-2022; 462-475
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