Cooperation in evolutionary games incorporated with extended Q-learning algorithm

Pinduo Long (), Qionglin Dai, Haihong Li () and Junzhong Yang ()
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Pinduo Long: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Qionglin Dai: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Haihong Li: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Junzhong Yang: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 03, 1-13

Abstract: Evolutionary game theory provides a platform to investigate the emergence of cooperation in population consisting of selfish agents. In this work, we study evolutionary games on networks in which agents cooperate or defect according to Q-learning algorithms with extended state space. Extended state space provides agents two types of information, local environment information based on the cooperation level in agents’ neighborhood and personal information based on the last action of agents. Through numerical simulations, we find that rich information on local environment tends to improve cooperation in the population no matter whether personal information is present or not. Moreover, we show that, for the same local environment information, the introduction of personal information may improve cooperation except for the situations with low amount of local environment information where personal information deteriorates cooperation in bad-condition environment. For the same total information, the absence of personal information promotes cooperation in bad-condition environment while the presence of personal information promotes cooperation in good-condition environment. By investigating the distributions and temporal behaviors of Q-values, we present explanations for the above statements. This work suggests an effective way of extending the state space in evolutionary games incorporated with Q-learning algorithm to enhance cooperation.

Keywords: Evolutionary game theory; cooperation; Q-learning; extended state space (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183124502048

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