With the development of artificial intelligence, multiagent algorithms have been applied to many real-time strategy games. Making plans on the human being is gradually passing away, especially in combat scenarios. Cognitive electronic warfare (CEW) is a complex and challenging work due to the sensitivity of the data sources. There are few studies on CEW. In the past, wargame simulations depended on differential equations and war theory, which resulted in high time and human resource costs. In the future, as other artificial intelligence theories are developed, artificial intelligence will play a more critical role in wargames. The capabilities of multiagent modeling to describe complex systems and predict actions in dynamic environments are superior to those of traditional methods. In this paper, we use a 3D wargame engine from China Aerospace System Simulation Technology Co., Ltd. (Beijing) named All Domain Simulation (ACS), which supports land, sea, and air combat scenarios, to simulate combat. In the simulations, there are several unmanned air vehicles (UAVs) as attackers and several radar stations as defenders, and both have the ability to detect the others. In the game, several UAVs need to learn to detect targets and track targets separately, and we train the UAV’s behavior by well-designed reward shaping and multiagent reinforcement learning (MARL) with LSTM. We improved the RDPG [1] algorithm and merged the MADDPG [2] and RDPG algorithms. From the experimental results, we can see that the effectiveness and accuracy of the algorithm have been greatly improved.

Earthquakes pose significant threats to human life and property, often triggering secondary disasters such as landslides, mudslides, and collapses. Although earthquake prediction remains a challenging global scientific problem, the study of seismic precursors plays a critical role. Earthquakes occur due to the instability and fracturing of underground rock layers when concentrated stress exceeds their strength limit. Borehole strainmeters provide direct observations of crustal strain, making pre-earthquake strain anomaly detection essential for precursor studies. In recent decades, variational autoencoders (VAEs) have been widely adopted for anomaly detection due to their powerful denoising capabilities. However, traditional VAE-based methods face difficulties in capturing both long-term heterogeneous patterns and fine-grained short-term trends simultaneously. To address this, we propose a new approach combining convolutional neural networks (CNN) with frequency-aware conditional VAE frameworks. The CNN extracts spatial dependencies among strain observation components, while frequency analysis improves temporal feature capture. By incorporating a target attention mechanism, our model selects the most relevant frequency-domain information to enhance reconstruction of both long-term and short-term trends. Experimental results on borehole strain data show that our model outperforms state-of-the-art methods. These findings confirm the practical value of our approach in overcoming current VAE-based detection limitations and emphasize the importance of integrating spatial and frequency representations in seismic precursor studies.