Promoting cooperation in the voluntary prisoner's dilemma game via reinforcement learning

Reinforcement learning technology has been empirically demonstrated to facilitate cooperation in game models. However, traditional research has primarily focused on two-strategy frameworks (cooperation and defection), which inadequately captures the complexity of real-world scenarios. To address this limitation, we integrated Q-learning into the prisoner's dilemma game, incorporating three strategies: cooperation, defection, and going it alone. We defined each agent's state based on the number of neighboring agents opting for cooperation and included social payoff in the Q-table update process. Numerical simulations indicate that this framework significantly enhances cooperation and average payoff as the degree of social-attention increases. This phenomenon occurs because social payoff enables individuals to move beyond narrow self-interest and consider broader social benefits. Additionally, we conducted a thorough analysis of the mechanisms underlying this enhancement of cooperation.