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

• URL: http://arxiv.org/abs/2504.06386v1
• Date: Tue, 08 Apr 2025 19:09:07 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 provide a bound on the probability of violating a safety property for a new task-specific policy in a model-free, episodic setup.<n>We also present SPoRt, which enables the user to trade off safety guarantees in exchange for task-specific performance.
• 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 novel theoretical results that provide a bound on the probability of violating a safety property for a new task-specific policy in a model-free, episodic setup: the bound, based on a `maximum policy ratio' that is computed with respect to a `safe' base policy, can also be more generally applied to temporally-extended properties (beyond safety) and to robust control problems. We thus present SPoRt, which also provides a data-driven approach for obtaining such a bound for the base policy, based on scenario theory, and which includes Projected PPO, a new projection-based approach for training the task-specific policy while maintaining a user-specified bound on property violation. Hence, SPoRt enables the user to trade off safety guarantees in exchange for task-specific performance. Accordingly, we present experimental results demonstrating this trade-off, as well as a comparison of the theoretical bound to posterior bounds based on empirical violation rates.

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