TY - JOUR
T1 - Topological optimization of continuous action iterated dilemma based on finite-time strategy using DQN
AU - Jin, Xiaoyue
AU - Li, Haojing
AU - Yu, Dengxiu
AU - Wang, Zhen
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2024
PY - 2024/6
Y1 - 2024/6
N2 - In this paper, a finite-time convergent continuous action iterated dilemma (CAID) with topological optimization is proposed to overcome the limitations of traditional methods. Asymptotic stability in traditional CAID does not provide information about the rate of convergence or the dynamics of the system in the finite time. There are no effective methods to analyze its convergence time in previous works. We made some efforts to solve these problems. Firstly, CAID is proposed by enriching the players’ strategies as continuous, which means the player can choose an intermediate state between cooperation and defection. And discount rate is considered to imitate that players cannot learn accurately based on strategic differences. Then, to analyze the convergence time of CAID, a finite-time convergent analysis based on the Lyapunov function is introduced. Furthermore, the optimal communication topology generation method based on the Deep Q-learning (DQN) is proposed to explore a better game structure. At last, the simulation shows the effectiveness of the proposed method.
AB - In this paper, a finite-time convergent continuous action iterated dilemma (CAID) with topological optimization is proposed to overcome the limitations of traditional methods. Asymptotic stability in traditional CAID does not provide information about the rate of convergence or the dynamics of the system in the finite time. There are no effective methods to analyze its convergence time in previous works. We made some efforts to solve these problems. Firstly, CAID is proposed by enriching the players’ strategies as continuous, which means the player can choose an intermediate state between cooperation and defection. And discount rate is considered to imitate that players cannot learn accurately based on strategic differences. Then, to analyze the convergence time of CAID, a finite-time convergent analysis based on the Lyapunov function is introduced. Furthermore, the optimal communication topology generation method based on the Deep Q-learning (DQN) is proposed to explore a better game structure. At last, the simulation shows the effectiveness of the proposed method.
KW - Convergence analysis
KW - Evolutionary game theory
KW - Lyapunov function
KW - Topological optimization
UR - http://www.scopus.com/inward/record.url?scp=85192386812&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2024.04.010
DO - 10.1016/j.patrec.2024.04.010
M3 - 文章
AN - SCOPUS:85192386812
SN - 0167-8655
VL - 182
SP - 133
EP - 139
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
ER -