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Self-adaptive PID has become a hotspot in the field of control, it can solve the problem that traditional PID can’t turning parameters. This paper proposed a new adaptive PID controller based on the Asynchronous Advantage Actor-Critic (A3C) algorithm. It used the multi-threaded and asynchronous learning style to train multiple agents of Actor-Critic (AC) structures in parallel. In order to achieve the best effect, each agent adapts a multilayer feedforward neural network to approximate strategy function and value function. In this way, they can search for the best parameter turning strategies in continuous motion space. Compared with the performance of others adaptive PID controllers, the results show that this method has the advantage of fast convergence and strong self-adaptability.