Container congestion remains a persistent operational challenge in seaports because berth, yard, and gate processes are tightly coupled, demand is volatile, and control actions often operate under delayed feedback. Reinforcement learning (RL) is increasingly proposed for adaptive terminal decision support, yet the literature still says little about the mechanism through which RL may reduce congestion in practice. This study therefore develops a simulation-based mechanism framework in which RL improves congestion outcomes primarily by increasing Operational Learning Stability (OLStab), defined here as the consistency and governability of learning-enabled operational decisions under variability and disruption. A queueing-based, gate-focused terminal simulator is used as the data-generating process, with gate congestion treated as a reduced-form proxy for broader terminal congestion pressure. The statistical layer is interpreted cautiously as an internal mechanism consistency check within synthetic data rather than as empirical causal identification. Results show that RL is strongly associated with higher OLStab and that OLStab is the dominant pathway linking RL to lower congestion pressure in the simulated environment. Logistics Efficiency (LE) is directionally consistent with congestion reduction in bivariate analysis but adds limited incremental mediation once OLStab is jointly modeled. The theorized moderation by Decision Latency Sensitivity (DLS) is not robustly recovered within the examined latency range. Overall, the study contributes a more bounded explanation of how RL may reduce congestion in a designed gate-focused terminal control environment and highlights learning stability as a practical screening criterion for future digital twin and pilot deployment studies.

Decision support for port-city freight gateways is challenging when operational information is limited to aggregated observations and decision indicators are researcher-constructed. This study develops an auditable offline scenario-evaluation framework using 76 monthly Port of Los Angeles observations from January 2020 to April 2026. Five interconnected proxy indices describe drift pressure (APD), information clarity (PED), predictability (GPH), operational learning stability (OLStab), and oscillation severity (GOS). Their algebraic dependence limits regression and pathway tests to internal consistency diagnostics. We evaluate five hypothetical response classes using four tabular reinforcement-learning algorithms, a maintain-release baseline, random actions, and an always-appointment-smoothing rule. A chronological 60-month training and 16-month testing design fits normalization, state thresholds, and reward standardization only on training observations. Across ten random seeds, learned policies reduce test-period proxy GOS by 0.76–1.14% relative to maintaining release; random actions achieve 3.64%, while always smoothing produces a stipulated 10.00% reduction. Fourteen test observations occupy states unseen during training and use a prespecified maintain-release fallback. Since action effects are imposed and subsequent monthly states do not depend on actions, these estimates describe scenario scoring rather than verified operational control. Independent physical validation and action-dependent transition data remain necessary.