Planner-Aware Consequence Feedback for Coupling Task Assignment and Route Planning in Dynamic Multi-Agent Rescue

Dynamic multi-agent rescue couples task assignment with route planning as haz-ards alter connectivity and traversal risk. We study how a learned dispatcher can use candidate routes returned by a fixed planner to inform target selection before commitment. We formulate Dynamic Multi-Rescuer Indoor Evacuation (DM-RIE), a centralized cooperative decision problem on dynamic multi-floor building graphs, and propose Planner-Aware Consequence Feedback (PACF), a modular planner-to-dispatcher interface. For each legal rescuer–target pair, a fixed plan-ner supplies currently feasible route candidates; PACF encodes their context and aggregates learned candidate scores into a target-logit correction. Mission-level rewards jointly train the dispatcher and PACF, while candidate generation, fea-sibility checking, and route selection follow fixed planner rules. Under a com-mon execution protocol and building-disjoint splits, evaluations over three train-ing seeds show that adding PACF increases mean Test rescue rates from 91.90% to 92.71% for the MAPDP-based dispatcher and from 90.51% to 93.48% for the MAPT-based dispatcher. Improvements vary across training seeds and task con-ditions. These results support the usefulness of planner-derived route context for learned target selection in the DMRIE benchmark.