Abstract
Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment. It is an important approach to solving sequential decision-making problems. By combining it with deep learning, deep reinforcement learning possesses powerful perception and decision-making capabilities and has been widely applied to various domains to tackle complex decision problems. Off-policy reinforcement learning separates exploration and exploitation by storing and replaying interaction experiences, making it easier to find global optimal solutions. Understanding how to utilize experiences is crucial for improving the efficiency of off-policy reinforcement learning algorithms. To address this problem, this paper proposes Z-Score Prioritized Experience Replay, which enhances the utilization of experiences and improves the performance and convergence speed of the algorithm. A series of ablation experiments demonstrate that the proposed method significantly improves the effectiveness of deep reinforcement learning algorithms.