Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

TL;DR

The paper presents UPSi, a novel safety filter for model-based reinforcement learning that uses probabilistic ensemble neural networks to provide rigorous safety guarantees during exploration.

Contribution

It introduces UPSi, a safety filter that explicitly quantifies uncertainty using probabilistic ensemble models and integrates safety constraints into MBRL frameworks.

Findings

Substantial safety improvements over prior neural network PSFs.

Maintains comparable performance to standard MBRL methods.

Effectively prevents model exploitation during exploration.

Abstract

Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability. Meanwhile, model-based RL (MBRL) methods routinely employ probabilistic ensemble (PE) neural networks to capture complex, high-dimensional dynamics from data with minimal prior knowledge. However, existing attempts to integrate PEs into PSFs lack rigorous uncertainty quantification. We introduce the Uncertainty-Aware Predictive Safety Filter (UPSi), a PSF that provides rigorous safety predictions using PE dynamics models by formulating future outcomes as reachable sets. UPSi introduces an explicit certainty constraint that prevents model exploitation and integrates seamlessly into common MBRL frameworks. We evaluate UPSi within Dyna-style MBRL on standard safe RL benchmarks and report substantial improvements in exploration safety over prior neural network PSFs while maintaining performance on par with standard MBRL. UPSi bridges the gap between the scalability and generality of modern MBRL and the safety guarantees of predictive safety filters.