part of the series on scheduling optimization in logistics with Multi-agent reinforcement learning (MARL). Here, I focus more on how the generalization was achieved. I recommend reading Part 1 first if you want to get a picture of the architectural and business context. The goal was for the model to generalize mid-mile processes and survive even in changing conditions. I realized this vision through three foundational concepts: Hybrid architecture abstracts the physical complexity Scale-invariant observations create a universal model input MARL makes the agents adaptable Spoiler alert : The first two concepts allow us to transfer agents easily between tasks, while the third one makes the agent adaptive within a single task and beyond. Let’s look at each one. Hybrid Architecture How to engineer a system capable of delivering robust solutions, even when moved into entirely new contexts?…