Interaction state Q-learning promotes cooperation in the spatial prisoner's dilemma game

Zhengzhi Yang, Lei Zheng, Matjaž Perc and Yumeng Li

Applied Mathematics and Computation, 2024, vol. 463, issue C

Abstract: Many recent studies have used reinforcement learning methods to investigate the behavior of agents in evolutionary games. Q-learning, in particular, has become a mainstream method during this development. Here we introduce Q-learning agents into the evolutionary prisoner's dilemma game on a square lattice. Specifically, we associate the state space of Q-learning agents with the strategies of their neighbors, and we introduce a neighboring reward information sharing mechanism. We thus provide Q-learning agents with the payoff information of their neighbors, in addition to their strategies, which has not been done in previous studies. Through simulations, we show that considering neighborhood payoff information can significantly promote cooperation in the population. Moreover, we show that for an appropriate strength of neighborhood payoff information sharing, a chessboard pattern emerges on the lattice. We analyze in detail the reasons for the emergence of the chessboard pattern and the increase in cooperation frequency, and we also provide a theoretical analysis based on the pair approximation method. We hope that our research will inspire effective approaches for resolving social dilemmas by means of sharing more information among reinforcement learning agents during evolutionary games.

Keywords: Evolutionary games; Cooperation; Prisoner's dilemma game; Reinforcement learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300323005337
Full text for ScienceDirect subscribers only

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:eee:apmaco:v:463:y:2024:i:c:s0096300323005337

DOI: 10.1016/j.amc.2023.128364

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().