Curriculum-Guided Reinforcement Learning for Energy-Efficient UAV-ISAC in Post-Disaster Search-and-Rescue Operations

Abstract

Uncrewed aerial vehicles (UAVs) are promising platforms for integrated sensing and communication (ISAC), but their limited onboard energy creates a strong coupling among sensing accuracy, communication quality, and propulsion cost. This paper proposes a curriculum-guided soft actor-critic (CG-SAC) framework with propulsion-aware reward shaping for energy-efficient UAV-ISAC, jointly optimizing the 3D trajectory, communication-sensing power split, and per-user power allocation. A rotary-wing propulsion model is incorporated to derive a closed-form propulsion-economic cruising speed, which is used to construct a propulsion-aware speed-shaping term within a normalized composite reward together with navigation, node-visiting, energy-efficiency, and constraint-penalty terms. A log-linear curriculum progressively tightens the communication, sensing, and proximity requirements during training. Across 2000 randomized scenarios, CG-SAC achieves an average energy efficiency of 0.72 Mbits/J, substantially outperforming the evaluated DRL baselines. Among successfully completed missions, it requires 107.6 steps on average, corresponding to a 66%--82% reduction in flight steps relative to the baselines. Crucially, the learned policy exhibits mission-aware speed adaptation by decelerating near service points and accelerating during transit, while achieving a 99.6% communication-rate satisfaction ratio at service instants. Ablation results further demonstrate the complementary roles of the reward components in balancing mission feasibility and energy efficiency.