Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness

• URL: http://arxiv.org/abs/2305.15602v1
• Date: Wed, 24 May 2023 22:22:19 GMT
• Title: Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness
• Authors: Song Bo, Bernard T. Agyeman, Xunyuan Yin, Jinfeng Liu (University of Alberta)
• Abstract summary: This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL. The robustness of the proposed approach is investigated in the presence of uncertainty. Results show a significant improvement in sampling efficiency during offline training and closed-loop stability guarantee in the online implementation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the advantages of utilizing the explicit form of CIS to improve stability guarantees and sampling efficiency. Furthermore, the robustness of the proposed approach is investigated in the presence of uncertainty. 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. This incorporation of CIS facilitates improved sampling efficiency during the offline training process. In the online stage, RL is retrained whenever the predicted next step state is outside of the CIS, which serves as a stability criterion, by introducing a Safety Supervisor to examine the safety of the action and make necessary corrections. The stability analysis is conducted for both cases, with and without uncertainty. 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 guarantee in the online implementation, with and without uncertainty.

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