The reinforcement learning model with heterogeneous learning rate in activity-driven networks

Agent’s learning behavior usually presents biased judgments influenced by many internal and external reasons, we incorporate an improved Q-learning algorithm in the reinforcement learning which is examined with the prisoner’s dilemma game in an activity-driven networks. The heterogeneous learning rate and ϵ-greedy exploration mechanism are taken into account while modeling decision-making of agents. Simulation results show the proposed reinforcement learning mechanism is conducive to the emergence of defective behavior, i.e. it could maximize one’s expected payoff regardless of its neighbors’ strategy. In addition, we find the temptation gain, vision level and the number of connected edges of activated agents are proportional to the density of defectors. Interestingly, when the inherent learning rate is small, the increase of exploration rate can demote the appearance of defectors, and the decrease of defectors is insignificant by increasing of exploration rate conversely.