(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The convergence of non-terrestrial and terrestrial communication infrastructures into a unified satellite–terrestrial integrated network represents a defining architectural ambition for sixth-generation systems. This integration promises ubiquitous coverage, enhanced resilience, and flexible service provisioning, yet it simultaneously introduces formidable resource orchestration challenges. Network slicing, which enables the coexistence of multiple logical networks over a shared physical substrate, is essential for delivering differentiated quality of service across such a heterogeneous domain. Traditional resource allocation heuristics struggle to cope with the high-dimensional state spaces, multi-timescale dynamics, and stringent latency constraints inherent to hybrid satellite–terrestrial slices. This paper presents a system-level investigation into the application of deep reinforcement learning for end-to-end slice resource scheduling in integrated space–ground architectures. The discussion focuses on structural trade-offs, deployment implications, multi-objective fairness, governance frameworks, and sustainability considerations rather than on mathematical formulations. An extensive analysis is offered of how centralized and distributed learning agents can reason about spectrum, beam, storage, and computational resources across moving satellite nodes and stationary terrestrial cells. The paper further examines robustness under orbital mobility and sudden link interruptions, explores governance mechanisms for cross-operator slicing, and positions future developments around digital twins, zero-trust security, and intent-based networking. The study provides a broad architectural discourse intended to guide the engineering of autonomous, resilient, and equitable slice schedulers in next-generation satellite–terrestrial systems.