SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning
Summary: arXiv:2604.09452v1 Announce Type: cross
Abstract: Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety-critical tasks. Often, deployment environments exhibit non-stationary dynamics or are subject to changing performance goals, requiring updates to the learned policy. This leads to a fundamental challenge: how to update an RL policy while preserving its safety properties on previously encountered tasks? The majority of current approaches either do not provide formal guarantees or verify policy safety only a posteriori. We propose a novel a priori approach to safe policy updates in continual RL by introducing the Rashomon set: a region in policy parameter space certified to meet safety constraints within the demonstration data distribution. We then show that one can provide formal, provable guarantees for arbitrary RL algorithms used to update a policy by projecting their updates onto the Rashomon set. Empirically, we validate this approach across grid-world navigation environments (Frozen Lake and Poisoned Apple) where we guarantee an a priori provably deterministic safety on the source task during downstream adaptation. In contrast, we observe that regularisation-based baselines experience catastrophic forgetting of safety constraints while our approach enables strong adaptation with provable guarantees that safety is preserved.
Introduction
As the use of reinforcement learning (RL) expands into critical domains such as autonomous driving, healthcare, and robotics, the need for safety in the deployment of RL agents has become paramount. The inherent challenge lies in the dynamic nature of these environments, where the conditions and objectives can shift, necessitating updates to the learned policies. The crux of the problem is how to ensure that these policy updates do not compromise the safety of previously encountered tasks.
Current Challenges in Policy Updates
Traditional methods for updating RL policies often fall short in providing robust safety guarantees. Many existing approaches:
- Do not offer formal safety guarantees during the policy update process.
- Only verify safety after the fact, which can lead to unforeseen failures in safety-critical applications.
The Rashomon Set Approach
To address these shortcomings, the SafeAdapt framework introduces the concept of the Rashomon set. This innovative approach defines a specific region within the policy parameter space that is guaranteed to satisfy safety constraints, based on the distribution of the demonstration data. By projecting policy updates onto this Rashomon set, SafeAdapt ensures:
- Formal, a priori safety guarantees for any RL algorithm utilized in the policy update process.
- Deterministic safety on the original task during subsequent adaptations, thus minimizing risks.
Empirical Validation
The efficacy of the SafeAdapt method was empirically validated through experiments in grid-world navigation environments, specifically Frozen Lake and Poisoned Apple. These experiments demonstrated that:
- SafeAdapt maintains safety guarantees during policy updates.
- Regularisation-based methods frequently suffer from catastrophic forgetting of safety constraints.
- SafeAdapt allows for efficient adaptation while ensuring that previously established safety standards are upheld.
Conclusion
In conclusion, SafeAdapt presents a significant advancement in the field of reinforcement learning, particularly for applications where safety is non-negotiable. By introducing a method to ensure provably safe policy updates, it paves the way for more reliable deployment of RL agents in complex, dynamic environments. The implications of this research are vast, promising enhanced safety and effectiveness in a variety of real-world applications.