SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL

Jacques Cloete; Nikolaus Vertovec; Alessandro Abate

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arXiv · 2504.06386

SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL

Jacques Cloete·Nikolaus Vertovec·Alessandro Abate

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Abstract

To apply reinforcement learning to safety-critical applications, we ought to provide safety guarantees during both policy training and deployment. In this work, we present theoretical results that place a bound on the probability of violating a safety property for a new task-specific policy in a model-free, episodic setting. This bound, based on a maximum policy ratio computed with respect to a 'safe' base policy, can also be applied to temporally-extended properties (beyond safety) and to robust control problems. To utilize these results, we introduce SPoRt, which provides a data-driven method for computing this bound for the base policy using the scenario approach, and includes Projected PPO, a new projection-based approach for training the task-specific policy while maintaining a user-specified bound on property violation. SPoRt thus enables users to trade off safety guarantees against task-specific performance. Complementing our theoretical results, we present experimental results demonstrating this trade-off and comparing the theoretical bound to posterior bounds derived from empirical violation rates.

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BibTeX RIS

Jacques Cloete, Nikolaus Vertovec, Alessandro Abate. 2025-06-23. SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL. https://arxiv.org/abs/2504.06386

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