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

• URL: http://arxiv.org/abs/2504.06386v2
• Date: Mon, 23 Jun 2025 10:50:00 GMT
• Title: SPoRt -- Safe Policy Ratio: Certified Training and Deployment of Task Policies in Model-Free RL
• Authors: Jacques Cloete, Nikolaus Vertovec, Alessandro Abate,
• Abstract summary: 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.<n>This bound can be applied to temporally-extended properties (beyond safety) and to robust control problems.<n>We present experimental results demonstrating this trade-off and comparing the theoretical bound to posterior bounds derived from empirical violation rates.
• Score: 54.022106606140774
• License: http://creativecommons.org/licenses/by/4.0/
• 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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