Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 11 Apr 2023.
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12 of 29 references.
References · 12
- 2017Safe Reinforcement Learning via Shieldingcited by 449
- 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
- 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
- 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
- 2019Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamicscited by 47
- 2020Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?cited by 34
- 2021Computationally Efficient Safe Reinforcement Learning for Power Systemscited by 20
- 2019Practical Reinforcement Learning of Stabilizing Economic MPCcited by 17
- 2021Learning Region of Attraction for Nonlinear Systemscited by 16
- 2016Computing Robust Controlled Invariant Sets of Linear Systemscited by 5
- 2021Robust economic model predictive control with zone trackingcited by 3
- 2020Computing robust control invariant sets of constrained nonlinear systems: A graph algorithm approachcited by 2
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Citing papers per year: 2023: 1.
1 paper.