Proximal policy optimization (PPO) is a stable deep reinforcement learning algorithm. The key process of the algorithm is to use clipped surrogate targets to limit step size updates. Experiments have found that when a clipping coefficient with optimal experience is employed, the upper bound of Kullback-Leibler (KL) divergence cannot be determined. This phenomenon is against the optimization theory of trust region. In this study, an improved PPO with double clipping boundaries (PPO-DC) algorithm is proposed. The algorithm adjusts the KL divergence based on two probability-based clipping boundaries and limits parameters to the trust region, so as to ensure that the sample data are fully utilized. In several continuous control tasks, the PPO-DC algorithm achieves better performance than other algorithms.