  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.
