LAUSR

Understanding cooperative behavior in multi-agent system is a research hotspot. In the context of pairwise interaction games, several researches have used reinforcement learning rules to successfully explain and predict the behavior of agents. However, multi-agent interactions are more general than two-agent interactions, and the effect of reward mechanism on behavior of agents is also ignored under the reinforcement learning rules. Therefore, this paper established a framework that combines the public goods game with reinforcement learning and adaptive reward. In that, public goods game is adopted to reflect the decision-making behavior of multi-agent interactions, self-regarding Q-learning emphasizes an experience-based strategy update, and adaptive reward focuses on the adaptability. We are mainly concentrating on the synergistic effects of them. It is remarkable that while self-regarding Q-learning fails to prevent the collapse of cooperation in the traditional public goods game, the fraction of cooperation increases significantly when the adaptive reward strategy is included. Meanwhile, the theoretical analysis results match well with the simulation results, which indicate that there is a specific reward cost to maximize the fraction of cooperation. Our findings may provide a new perspective for establishing cooperative reward mechanisms in social dilemmas.