Reinforcement learning (RL) is an area of significant research interest, and safe RL in particular is attracting attention due to its ability to handle safety-driven constraints that are crucial for real-world applications of RL algorithms. This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the benefits of CIS to improve stability guarantees and sampling efficiency. The approach consists of two learning stages: offline and online. In the offline stage, CIS is incorporated into the reward design, initial state sampling, and state reset procedures. In the online stage, RL is retrained whenever the state is outside of CIS, which serves as a stability criterion. A backup table that utilizes the explicit form of CIS is obtained to ensure the online stability. To evaluate the proposed approach, we apply it to a simulated chemical reactor. The results show a significant improvement in sampling efficiency during offline training and closed-loop stability in the online implementation.

本文提出了一种新的强化学习培训方法，称为控制不变集（CIS）增强RL，该方法通过控制不变集的优势改善稳定性和采样效率，其中包括离线和在线的两个学习阶段，使用CIS来设计奖励、采样初始状态和状态重置程序，并在状态不在CIS时重新训练 RL，以确保在线稳定；在模拟化学反应器中进行应用，结果表明，在离线训练期间，采样效率显著提高，并且实现了闭环稳定性。

基于控制不变集增强型强化学习的过程控制：提高采样效率和保证稳定性