Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

Problem
This paper addresses the limitations of existing predictive safety filters (PSFs) in deep reinforcement learning (RL), which typically rely on first-principles models or Gaussian processes, thus hindering scalability and broader applicability. The authors note that while model-based reinforcement learning (MBRL) methods utilize probabilistic ensemble (PE) neural networks to model complex dynamics, previous attempts to integrate these into PSFs have not adequately addressed uncertainty quantification. This work presents the Uncertainty-Aware Predictive Safety Filter (UPSi) as a solution, which is currently a preprint and unreviewed.

Method
UPSi enhances PSFs by employing PE dynamics models to predict future outcomes as reachable sets, thereby providing a rigorous framework for safety predictions. The architecture integrates an explicit certainty constraint that mitigates the risk of model exploitation during exploration. UPSi is designed to be compatible with Dyna-style MBRL frameworks, allowing it to leverage existing MBRL methodologies while ensuring safety. The authors do not disclose specific details regarding the training compute or the exact architecture of the PE models used, but they emphasize the integration of uncertainty quantification into the PSF framework.

Results
The evaluation of UPSi is conducted on standard safe RL benchmarks, where it demonstrates substantial improvements in exploration safety compared to prior neural network PSFs. The authors report that UPSi maintains performance levels comparable to standard MBRL approaches, indicating that it does not sacrifice efficiency for safety. Specific effect sizes or numerical results are not provided in the abstract, but the emphasis on “substantial improvements” suggests a significant enhancement over baseline methods.

Limitations
The authors acknowledge that while UPSi improves safety predictions, it may still be limited by the underlying assumptions of the PE models and the complexity of the environments in which it is deployed. They do not discuss potential computational overhead introduced by the additional safety constraints or the scalability of UPSi in highly dynamic or uncertain environments. Furthermore, the reliance on Dyna-style MBRL may restrict its applicability to other MBRL paradigms.

Why it matters
The introduction of UPSi represents a critical advancement in the integration of safety mechanisms within MBRL frameworks, particularly in environments where safety is paramount. By bridging the gap between the scalability of PE models and the safety guarantees of PSFs, this work opens avenues for further research into robust RL systems that can operate safely in real-world applications. The implications extend to autonomous systems, robotics, and any domain where safe exploration is essential, potentially influencing future developments in safe RL methodologies.

Authors: Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, Sebastian Trimpe
Source: arXiv:2604.26836
URL: https://arxiv.org/abs/2604.26836v1