Offline Proxy-Based Reinforcement Learning for Port-City Freight Gateway Operations: Macro-Strategic Decision Support at the Port of Los Angeles

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.