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Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma

We present tournament results and several powerful strategies for the Iterated Prisoner’s Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many...

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Detalles Bibliográficos
Autores principales: Harper, Marc, Knight, Vincent, Jones, Martin, Koutsovoulos, Georgios, Glynatsi, Nikoleta E., Campbell, Owen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5724862/
https://www.ncbi.nlm.nih.gov/pubmed/29228001
http://dx.doi.org/10.1371/journal.pone.0188046
Descripción
Sumario:We present tournament results and several powerful strategies for the Iterated Prisoner’s Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also.