In the field of deep reinforcement learning, particularly for high-dimensional continuous tasks, efficiently utilizing limited training data, preventing overfitting, and enhancing the model’s generalization ability are crucial research challenges. Traditional reinforcement learning algorithms typically rely on a single experience replay buffer, which, when applied to high-dimensional continuous state and action spaces, often faces low exploration efficiency and insufficient sample utilization. A reinforcement learning experience replay mechanism based on sample distinctiveness called distinctive experience replay (DER) is proposed. This mechanism selects samples with notable distinctiveness for experience replay. The core concept of DER is to identify and select significantly distinctive samples during training and store them in a dedicated experience pool. This mechanism not only effectively utilizes diverse samples to prevent neural network overfitting but also enhances the agent’s learning efficiency and decision-making quality in complex environments. Experimental results show that DER significantly improves the agent’s learning efficiency and final performance in classic reinforcement learning environments.