In deep reinforcement learning, the deep Q-network algorithm seriously overestimates the action value, which degrades the performance of agents.The double deep Q-network and dueling network structure can partially alleviate the impact of overestimation, sometimes the former one even underestimate the action value.Here, a Weighted Dueling Double Deep Q-Network (WD3QN) algorithm is proposed, in which the improved double estimators and dueling network structure are combined into the deep Q-network, and the learned possible action values are weighted to produce the final action value, which can effectively reduce the estimation error.Finally, the algorithm is applied to the classical CartPole control problem on the open AI Gym platform.The simulation results show that compared with other existing algorithms, the proposed algorithm has better learning effect, convergence and training speed.