Implementing Probabilistic Safety Guardrails via Constrained Reinforcement Learning and Large Language Model Reasoning for Risk Aware Autonomous Decision Making - Warren Beaumont (Computational Intelligence Systems, 2026)
The integration of autonomous systems into critical infrastructure necessitates a paradigm shift from performance-centric optimization to risk-aware decision-making architectures. Traditional reinforcement learning frameworks often struggle with high-dimensional uncertainty and the "black-box" nature of neural policy execution, particularly when deployed in socio-technical environments where safety violations carry catastrophic consequences. This paper proposes a novel architectural synthesis that implements probabilistic safety guardrails by merging the mathematical rigor of Constrained Reinforcement Learning (CRL) with the semantic reasoning capabilities of Large Language Models (LLMs). We explore the structural trade-offs inherent in designing a dual-track system where LLMs serve as high-level symbolic reasoners that interpret complex safety policies, while CRL agents execute low-level control under strict probabilistic constraints. This research emphasizes the system-level governance required to manage the interaction between stochastic learning processes and deterministic safety boundaries. We analyze the deployment challenges of such hybrid infrastructures, focusing on robustness against out-of-distribution scenarios and the sustainability of human-in-the-loop oversight. Furthermore, the discussion extends to the policy implications of delegating ethical and safety-critical judgements to semi-autonomous reasoning modules. By examining case illustrations in intelligent transportation and industrial automation, this study provides a comprehensive roadmap for developing trustworthy autonomous systems that balance operational efficiency with rigorous, verifiable safety guardrails. The findings suggest that semantic reasoning acts as a critical bridge between numerical optimization and human-centric safety standards, ensuring that autonomous decision-making remains aligned with broader societal values and regulatory requirements.