mlsanityData to 14 Oct 2026

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

arXiv 2609.01764 ↗First posted 1 Sep 2026cs.NI cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 30 references that are in the map. First posted on arXiv 1 Sep 2026.

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9 of 30 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  5. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  6. 2023Integrated Sensing and Communications: Recent Advances and Ten Open Challengescited by 14
  7. 2023Fundamental CRB-Rate Tradeoff in Multi-Antenna ISAC Systems with Information Multicasting and Multi-Target Sensingcited by 5
  8. 2023SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systemscited by 3
  9. 2020Energy Model for UAV Communications: Experimental Validation and Model Generalizationcited by 2

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  1. 2026AERIS: Offline Policy Improvement for Multi-UAV Integrated Sensing and Communication
  2. 2025Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms
  3. 2025Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach
  4. 2024Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach
  5. 2026AC-MASAC: An Attentive Curriculum Learning Framework for Heterogeneous UAV Swarm Coordination
  6. 2026Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
  7. 2026Queue-Aware Graph Reinforcement Learning for UAV-ISAC-Assisted Maritime Data Collection
  8. 2026Deep Reinforcement Learning for Adaptive Power Allocation in ISAC Systems with Mobile Target