Multi-agent simulation based on deep reinforcement learning can simulate realistic economic agents well, and can play an important role in research work on complex economic system. In the simulated economic system model, DQN is used to handle the decision-making of economic agents, and their behavior strategies are continuously improved with the accumulation of experience as they interact with the environment. However, the action selector used by economic agents can affect their policy changes. Taking this problem into consideration, this paper did research on the action selector used by economic agents. Through simulation experiments, we compare the behavior policy of economic agents using different action selectors including the traditional ε-greedy and a novel NoisyNet method. The results show that an appropriate action selector enables economic agents to explore more widely and comprehensively, and learn a better policy.