关键词: 深度强化学习, 过估计, 更新目标, 动态融合

Abstract: Aiming at the problem of overestimation in deep reinforcement learning algorithm, a target dynamic fusion mechanism is proposed. Based on the Deep [Q] Networks（DQN） algorithm, an improvement is proposed to reduce the overestimation in DQN algorithm by incorporating the update target of Sarsa algorithm, while retaining the DQN algorithm to speed up the learning process, dynamically combining the respective advantages of the DQN algorithm and the Sarsa algorithm, the DTDQN （Dynamic Target Deep [Q] Network） algorithm is proposed. The experiment of Carteole control problem on OpenAI Gym with open platform is carried out. The results show that DTDQN can effectively reduce the overvalue of the function, and improve the learning performance and the training stability obviously.

Key words: deep reinforcement learning, overestimation, update target, dynamic fusion