Learn for Variation: Variationally Guided AAV Trajectory Learning in Differentiable Environments

• URL: http://arxiv.org/abs/2603.18853v1
• Date: Thu, 19 Mar 2026 12:57:42 GMT
• Title: Learn for Variation: Variationally Guided AAV Trajectory Learning in Differentiable Environments
• Abstract summary: Learn for Variation (L4V) is a gradient-informed trajectory learning framework.<n>It replaces high-variance scalar reward signals with dense and analytically grounded policy gradients.<n>L4V consistently outperforms representative baselines in mission completion time, average transmission rate, and training cost.
• Score: 21.594684328904403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous aerial vehicles (AAVs) empower sixth-generation (6G) Internet-of-Things (IoT) networks through mobility-driven data collection. However, conventional reward-driven reinforcement learning for AAV trajectory planning suffers from severe credit assignment issues and training instability, because sparse scalar rewards fail to capture the long-term and nonlinear effects of sequential movements. To address these challenges, this paper proposes Learn for Variation (L4V), a gradient-informed trajectory learning framework that replaces high-variance scalar reward signals with dense and analytically grounded policy gradients. Particularly, the coupled evolution of AAV kinematics, distance-dependent channel gains, and per-user data-collection progress is first unrolled into an end-to-end differentiable computational graph. Backpropagation through time then serves as a discrete adjoint solver, which propagates exact sensitivities from the cumulative mission objective to every control action and policy parameter. These structured gradients are used to train a deterministic neural policy with temporal smoothness regularization and gradient clipping. Extensive simulations demonstrate that L4V consistently outperforms representative baselines, including a genetic algorithm, DQN, A2C, and DDPG, in mission completion time, average transmission rate, and training cost

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