Feasibility-Preserving Reinforcement Learning for Dynamic Heterogeneous Multi-UAV Task Allocation with Temporal Dependencies

Coordinating heterogeneous unmanned aerial vehicles (UAVs) requires allocating complementary capabilities to temporally dependent tasks. We study a dynamic allocation model in which the dependency structure is known in advance and all other task information is revealed on arrival. The proposed framework combines a graph-transformer policy trained with proximal policy optimization, action masking, and ordered commitment to support structurally admissible allocation decisions. On a 229-scene benchmark, the method achieves higher mean recognized and Oracle-relative completion than the compared implementations, with lower completion variability across three seeds than MAPPO, and remains competitive from 12 to 40 UAVs. Within a separate ablation configuration, removing masking or ordered commitment produces the largest completion decreases. These results compare the reported configurations combining learned allocation with local structural and capacity checks.