Do Faithful Explanations Survive Disruption? Interpretable RL in Carbon‑Aware Dual‑Source Inventory Control

The aim of interpretable reinforcement learning is to achieve safer and more reliable decision-making through the use of transparent policies. We look at this assumption in the case of carbon-aware dual-source inventory control, where a product can be restocked quickly by means of high-emission air freight or more slowly by using lower-emission rail. We have developed a simulator which incorporates ordering costs, holding costs, backlog penalties, and partial Scope 3 emissions, and we evaluate the policies under a number of disruption scenarios such as demand spikes, supplier outages, lead-time shocks, low starting inventory, and longer planning horizons. The policy architectures employed for training PPO consist of a dense MLP, a scalar Neural Additive Model (NAM), and an interaction-NAM. With respect to the NAM-based policies, it is possible to carry out an exact decomposition of each action into its named feature and interaction components. We examine the robustness of these attributions for different training seeds and for two inventory configurations, and we compare the learned policies with a tuned dual-index heuristic, the nominal and service-target model predictive control, and a downside-penalized PPO baseline on a separate robustness grid. In the experiments, the interaction-NAM policies give rise to good action decompositions and generate interaction-importance rankings which are fairly stable regardless of the seed used. Nevertheless, the dual-index heuristic is still competitive or performs better in many disruption scenarios, and neither the interaction-NAM nor the downside-penalized PPO is able to close the gap. Furthermore, a post-training carbon-accounting sensitivity analysis indicates that the reported emissions are materially influenced by the accounting assumptions even when the policies remain unchanged. All of this suggests that accurate explanations do allow one to examine the trade-offs in this inventory setting, but they do not by themselves ensure robust control when disruptions occur.