Due to the dynamic complexities of the multi-unmanned vessel target assignment problem at sea, especially when addressing moving targets, traditional optimization algorithms often fail to quickly find an adequate solution. To overcome this, we have developed a multi-agent reinforcement learning algorithm. This approach involves defining a state space, employing preferential experience replay, and integrating self-attention mechanisms, which are applied to a novel offshore unmanned vessel model designed for dynamic target allocation. We have conducted a thorough analysis of strike positions and times, establishing robust mathematical models. Additionally, we designed several experiments to test the effectiveness of the algorithm. The proposed algorithm improves the quality of the solution by at least 30% in larger scale scenarios compared to the genetic algorithm (GA), and the average solution speed is less than 10% of the GA, demonstrating the feasibility of the algorithm in solving the problem.

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient’s personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately.

Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.

This paper addresses the heterogeneous multi-UAV task scheduling problemwith multi-stage precedence, resource, and spatio-temporal coordination constraints. Taking a common three-phase reconnaissance-delivery-assessmentmission as a running example, we formulate this problem as a Mixed-IntegerLinear Program (MILP) and prove its strong NP-hardness. To solve it efficiently, we develop a graph-based reinforcement learning framework thatmodels the scheduling process as a semi-Markov decision process over adynamic heterogeneous graph. The policy employs an autoregressive decomposition that separates UAV selection, task assignment, and resourceallocation, effectively reducing the combinatorial action space. A dynamicmasking mechanism guarantees feasibility and prevents deadlocks duringexecution, while the model is trained via proximal policy optimization witha centralized critic. Experiments demonstrate near-optimal performance onsmall instances compared to an exact solver, and significant improvements overclassical meta-heuristics on larger problems, all while maintaining real-timeinference.

Autonomous stealth navigation through modern Integrated Air Defense Systems requires Unmanned Aerial Vehicles (UAVs) to minimize radar detectability while maintaining efficient flight. This poses a severe continuous-control challenge under partial observability, as the dynamic radar cross-section (RCS) is highly coupled with the aircraft’s real-time attitude. In this paper, we formulate this task as a Partially Observable Markov Decision Process (POMDP) and propose an attention-guided deep reinforcement learning framework. Specifically, we design a cross-attention state encoder to adaptively process variable-length, localized radar warning signals, enabling dynamic threat prioritization. The control policy is optimized using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to generate high-precision continuous lateral maneuvers. Extensive simulations demonstrate that our proposed TD3-Att framework achieves improved convergence stability, higher average returns, and robust zero-shot generalization compared to DDPG and SAC baselines. Notably, the learned policy steers the aircraft along low-RCS heading/aspect angles, providing an effective continuous-control solution for navigating contested electromagnetic environments.