Continuous action iterated dilemma with data-driven compensation network and limited learning ability

This paper proposes a continuous action iterated dilemma (CAID) in evolutionary game theory with a data-driven compensation network and limited learning ability that considers both players' differences and unknown environment effects. In the traditional dynamic model of CAID, players have identical learning abilities and ignore the influence caused by the environment, which is inconsistent with real society. Therefore, we study the limited learning ability of CAID and the unknown learning mechanism caused by the environment to overcome these problems. Firstly, we propose the dynamic model of limited learning ability for CAID to reveal the law of cooperative evolution in the case when the learning abilities of players are varied. Considering the unknown learning mechanism of players, we adopt the data-driven compensation network to confront the effects of unknown dynamics caused by the environment. In addition, based on the limited learning ability and data-driven compensation network of players, the Lyapunov function is designed to prove the convergence of the CAID, avoiding the high computational complexity caused by the eigenvalues of the Jacobin matrix. In this case, simulations based on two classical dynamic model of evolutionary game theory are carried out to show the effectiveness of our proposed method.