Industrial maintenance systems often consist of multiple interacting assets that share limited resources, making it hard to balance reliability and operational cost within a single decision framework.\ \ Recent studies have concentrated on reinforcement learning (RL) for maintenance scheduling, yet direct comparisons with traditional planning approaches under identical experimental conditions remain scarce.\ \ In this work we use run‑to‑failure data from bearing assets to empirically compare planning and RL for multi‑asset maintenance, focusing on how each method trades preventive actions against tolerable failures across a range of failure‑penalty scenarios.\ \ The results reveal a consistent behavioral gap driven by objective formulation. Planning treats reliability as a hard constraint, yielding zero‑failure policies whose total cost is largely insensitive to the magnitude of the penalty. RL, by optimizing expected cost, shifts the balance between preventive maintenance and occasional failures as penalties vary—achieving lower costs under low‑penalty regimes but still producing non‑zero failures even when penalties are high.\ \ To improve RL’s reliability we explore lightweight constraint mechanisms such as reward shaping and action masking, which can modestly reduce failure rates.\ \ From a practical standpoint, planning is preferable when strict reliability is required and deployment horizons are short, whereas RL may be more cost‑effective when limited failures are acceptable and long‑run operational efficiency is prioritized. The two approaches are therefore complementary.\ \ Beyond these findings, the unified benchmark protocol introduced here aligns the environment, cost model, and evaluation metrics, offering a reusable template for comparing decision‑making methods in other maintenance contexts.\ \ Review