Spatial structure might impede cooperation in evolutionary games with reinforcement learning

Qi Shi (), Shilin Xiao (), Qionglin Dai, Haihong Li () and Junzhong Yang ()
Additional contact information
Qi Shi: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China
Shilin Xiao: 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), 2022, vol. 33, issue 12, 1-13

Abstract: Cooperation has attracted considerable attention in recent years. In order to explain altruistic cooperation behaviors that emerged in social dilemmas, a large number of mechanisms have been proposed under the framework of traditional evolutionary game (EG) theory, especially network reciprocity, which has achieved great success. On the other hand, the design of AI algorithm provides a new idea for agents’ decision-making behaviors. The influence of multi-agent reinforcement learning (RL) on cooperation has also received a lot of attention. However, the study of EG with AI algorithm in a population located on spatial structures has not been thoroughly considered. In this paper, we incorporate RL into EGs conducted on spatial structures. By numerical simulations, we find that, in comparison to the well-mixed case, cooperation might be impeded in the population located on spatial structures when the agents update their strategies by adopting Q-learning algorithm instead of pairwise imitation that widely used in traditional EGs. We further reveal the mechanism for the evolution of cooperation in the EGs with Q-learning algorithm, by investigating the distributions of the Q-tables held by agents in the population. Our findings may help understand the different outcomes on spatial structures in EGs with Q-learning algorithm when compared with that in traditional EGs.

Keywords: Evolutionary game; reinforcement learning; cooperation; social dilemma (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183122501686
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:33:y:2022:i:12:n:s0129183122501686

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183122501686

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().