关键词: 状态优先度, TD误差, 离散度, 优先经验回放, 学习效率

Abstract: In order to address the path planning problem of intelligent agents in complex environments, this paper proposes an online off policy deep reinforcement learning algorithm model based on an improved prioritized experience replay method. Firstly, the model utilizes a flexible action evaluation algorithm to achieve collision free path planning for the intelligent agent by designing the state space, action space, and reward function. Secondly, by calculating the sample mixing priority using the sample priority and TD error, a measure of sample diversity is obtained, and an improved prioritized experience replay method based on the flexible action evaluation algorithm is proposed to enhance the learning efficiency of the model. The simulation experimental results validate the effectiveness of the proposed improved flexible action evaluation algorithm under various parameter combinations and the superiority of the improved prioritized experience replay method in model learning efficiency for continuous control tasks

Key words: state priority, TD error, diversity, prioritized experience replay, learning efficiency