Surviving High Uncertainty in Logistics with MARL

To survive high uncertainty in logistics, a hybrid system uses Reinforcement Learning for high-level strategic decisions and Linear Programming for detailed execution tasks like packing. This approach achieves remarkable generalization by feeding agents scale-invariant observations, such as the percentage of the daily workload instead of raw package counts. This