2026· International Conference on Simulation and Modeling Methodologies, Technologies and Applications· pp. 595-607· 0 citations· 24 references
Computer Science
TL;DR
A learning-guided neuro-symbolic orchestration framework in which reinforcement learning operates at a meta level to select among heterogeneous symbolic solvers within a closed simulation loop is proposed.
Abstract
: Multi-unmanned Aerial Vehicle (multi-UAV) missions require task assignment strategies that adapt to dynamic urgency, threats, and resource constraints. Fixed heuristics lack flexibility, while end-to-end learned policies often omit explicit safety and verification mechanisms. This paper proposes a learning-guided neuro-symbolic orchestration framework in which reinforcement learning operates at a meta level to select among heterogeneous symbolic solvers within a closed simulation loop. Candidate assignments are regulated through a dual-layer verification mechanism comprising a feasibility filter and an episode-level simulator-based evaluator. The meta-decision layer is trained using a Double Deep Q-Network (Double DQN) with rewards reflecting completion, expiration, battery consumption, and threat exposure. Experiments across diverse scenarios demonstrate the effectiveness of adaptive solver selection and improved safety–efficiency trade-offs compared to fixed baselines.
Multi-UAV swarm coordination requires high-level task refinement across heterogeneous game-like operation segments with different controllers, risks, and time-dependent rewards. Existing hand-written refinement rules are difficult to tune when an intermediate action has no direct reward but changes downstream losses an...
Chen Wang, Yin-Xuan Huang, Lai-Long Luo et al.· 2026 12th International Conf...· 0 citations
A multi-agent deep reinforcement learning framework that addresses issues through coordinated exploration, demonstration exploitation, safe curriculum scheduling, and structure-aware generalisation is proposed, demonstrating strong performance in collaboration success rate, navigation robustness, zero-shot cross-scenar...
Ensuring QoS provisioning in low-altitude wireless networks requires UAV positioning and navigation strategies that adapt to dynamic environments and generalizes across heterogeneous network scenarios. This paper proposes a digital twin (DT)-assisted meta reinforcement learning framework for multi-agent UAV trajectory...
Jiayuan Huang, E. Tucker, Ruozhou Yu et al.· International Conference on...· 0 citations
We address the problem of multi-spacecraft formation acquisition by comparing a traditional LQR+APF controller with a reinforcement learning based agent. The scenario demonstrates maneuvering three deputy satellites under discretized Hill-Clohessy-Wiltshire dynamics. The operational environment imposes hard keep-out an...
Onur Aytekin, N. Ergin, Kullen W. Waggoner· National Aerospace and Elect...· 0 citations
HYPER-GNC, a multi-task reinforcement learning framework in which a hypernetwork maps physics-informed task embeddings to the weights of a shared actor-critic policy, enabling a single compact controller to master four distinct GNC tasks: velocity tracking, docking, inspection, and navigation with obstacle avoidance.
Ricard M. Castan, Aman Arora, Antoine Richard et al.· arXiv.org· 1 citation
The proposed hierarchical multi-agent proximal policy optimization framework can reduce total airlines' operational costs—including direct operating cost and capital cost and achieves a computation speedup in comparison with a conventional optimization baseline.
Li-Jing Liu, James M. Shihua, Qi-Yu Yan et al.· MATEC Web of Conferences· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
