EIRM-RL : epistemic integrity risk monitoring inspired safe reinforcement learning for trustworthy autonomous navigation

Abstract

Reinforcement learning (RL) has shown great potential for autonomous navigation within internet of things (IoT) environments, where various and changing uncertainties pose significant challenges for safe, real-world deployment. Existing safe RL methods typically employ heuristic constraints while neglecting the combined impact of multiple uncertainty sources, reducing robustness and interpretability. Drawing on concepts from global navigation satellite system (GNSS) integrity monitoring, this paper proposes an epistemic integrity risk monitoring reinforcement learning (EIRM-RL) framework to enable trustworthy autonomous navigation under uncertainty. EIRM-RL extends the GNSS protection level concept to RL by utilizing an assembled world model that quantifies and incorporates sensor noise, systematic bias, and epistemic uncertainty. Furthermore, the framework continuously monitors a dynamic epistemic risk probability, which is incorporated into policy optimization as an adaptive safety constraint via Lagrangian duality. This method enables the agent to proactively avoid hazards and effectively balance safety and performance, even in highly uncertain environments. Extensive experiments demonstrate that EIRM-RL achieves superior success rates, collision avoidance, and robustness compared to state-of-the-art safe RL methods, while maintaining high efficiency.