Thursday 03 July 2025
A team of researchers has developed a new framework for verifying and falsifying safety properties in reinforcement learning policies, a crucial step towards deploying these AI systems in high-stakes environments.
Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. It’s been used to great effect in games and simulations, but its deployment in real-world applications, such as autonomous vehicles or healthcare, requires a level of safety and reliability that isn’t currently achievable.
The problem is that reinforcement learning policies are often designed to maximize rewards, rather than ensuring safety. This means that they may take risks or behave unexpectedly when faced with unexpected situations.
To address this issue, the researchers have developed a hybrid framework that combines explainable abstraction, probabilistic model checking, and risk-aware falsification. The first step is to construct an abstract graph of the reinforcement learning policy using a technique called Comprehensible Abstract Policy Summarization (CAPS). This graph provides a human-interpretable representation of the policy’s behavior.
Next, the researchers use a probabilistic model checker called Storm to verify whether the policy satisfies certain safety properties. If the model checker finds any violations, it returns an interpretable counterexample trace that shows how the policy fails the safety requirement.
However, if the model checker doesn’t find any violations, the researchers can’t be sure that the policy is safe. This is because the abstraction and coverage of the offline dataset may not be comprehensive enough to detect all potential failures.
To address this issue, the researchers use a risk-aware falsification strategy that prioritizes searching in high-risk states and regions underrepresented in the trajectory dataset. They also provide PAC-style guarantees on the likelihood of uncovering undetected violations.
Finally, the researchers incorporate a lightweight safety shield that switches to a fallback policy at runtime when the risk exceeds a certain threshold. This allows them to mitigate failures without retraining the reinforcement learning model.
The framework was tested in several safety-critical domains, including autonomous driving and medical applications. The results show that the framework is able to detect significantly more safety violations than uncertainty-based or fuzzy-based search methods, and uncover a wider range of counterexamples.
This is an important step towards deploying reinforcement learning policies in high-stakes environments. By ensuring the safety and reliability of these AI systems, we can unlock their potential to improve our lives while minimizing the risk of accidents or harm.
Cite this article: “Verifying Safety Properties in Reinforcement Learning Policies”, The Science Archive, 2025.
Reinforcement Learning, Safety Properties, Machine Learning, Autonomous Vehicles, Healthcare, Explainable Abstraction, Probabilistic Model Checking, Risk-Aware Falsification, Safety Shield, Pac-Style Guarantees
