Safety Guarantees in Zero-Shot Reinforcement Learning for Cascade Dynamical Systems
This paper considers the problem of zero-shot safety guarantees for cascade dynamical systems. These are systems where a subset of the states (the inner states) affects the dynamics of the remaining states (the outer states) but not vice-versa. We define safety as remaining on a set deemed safe for all times with high probability. We propose to train a safe RL policy on a reduced-order model, which ignores the dynamics of the inner states, but it treats it as an action that influences the outer state. Thus, reducing the complexity of the training. When deployed in the full system the trained policy is combined with a low-level controller whose task is to track the reference provided by th...
| Authors | Shima Rabiei, Sandipan Mishra, Santiago Paternain |
|---|---|
| Primary category | cs.AI |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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