Uncertainty-aware zero-touch drift management for trustworthy DRL in 5G/6G networks

As the push toward fully autonomous 5G and future 6G networks accelerates, Deep Reinforcement Learning (DRL) has emerged as a cornerstone of intelligent decision-making, enabling real-time adaptability and self-optimization. However, this promise is increasingly overshadowed by a critical and underexamined risk: DRL Drift, which refers to sudden and often opaque degradation in agent performance after deployment. This phenomenon jeopardizes the reliability and trustworthiness of DRL systems operating in dynamic, real-world telecom environments. Despite growing adoption of DRL in both research and industry, the issue of DRL Drift remains largely overlooked in major telecommunications standards such as the 3rd Generation Partnership Project (3GPP) and the European Telecommunications Standards Institute (ETSI). To address this challenge, we propose a novel Zero-Touch Drift Management framework, developed in alignment with the ETSI closed-loop reference architecture. At its core lies the Composite Drift Index, a unified, domain-agnostic metric that combines key performance indicators, state-action transitions, and uncertainty estimation to enable proactive detection of degradation. Extensive evaluations in a representative network-slicing scenario demonstrate up to 23.4% higher detection accuracy than baseline methods, with strong generalization across diverse DRL approaches. This work offers the first standards-aligned solution to enhance DRL resilience against drift in next-generation networks.