Interpretable group gated priority traces for scalarized multi-objective reinforcement learning in autonomous drone operations

Autonomous drones are increasingly deployed for navigation, inspection, and monitoring in urban building and infrastructure environments that are dynamic, partially observable, and safety critical. These missions must balance conflicting objectives such as goal completion, wind avoidance, collision avoidance, signal coverage, and flight efficiency, making Multi-Objective Reinforcement Learning (MORL) an attractive control method. However, current explainability methods rarely examine how MORL policies prioritize different sensor channels during urban drone operations, leaving objective trade-offs and input priorities opaque to human operators. This paper introduces a lightweight group-gating architecture that augments MORL policies with an interpretable priority interface. The module aggregates raw observations into several meaningful categories (goal information, kinematics, wind, position, signal coverage, penalties, obstacle distance) and learns a gate vector that reweights these groups at every decision step. Integrated into a Proximal Policy Optimization (PPO) agent and evaluated in high-fidelity Unity simulations of urban operations with dynamic wind fields, the architecture preserves task performance while revealing stable priority patterns. Based on the results, three main findings emerge. First, the group-gating layer preserves asymptotic reward and value loss relative to ungated baselines. Second, gate dynamics exhibit dual-mode behavior, with a shared component that tracks global task difficulty and category-specific reallocations that differentiate wind and obstacle distance. Third, observation priorities align with environmental dynamics, with Dynamic Time Warping analysis showing 39% improved alignment for wind and 19% for obstacle distance when tracking changes rather than absolute levels. The resulting protocol provides a basis for real-time monitoring and for exploring adaptive sensor scheduling and early fault-detection heuristics in autonomous urban drone operations.