## 1 Introduction  
The propagation of electromagnetic waves is closely related to the atmospheric environment. It is mainly influenced by factors such as atmospheric refraction, temperature, humidity, air pressure, and ionospheric effects [1]. Atmospheric refraction causes the propagation path of radio waves to bend, especially when there are significant changes in temperature and air pressure. Increased humidity leads to signal attenuation, which is particularly pronounced in higher frequency bands (such as microwaves and millimeter waves). The ionosphere in the atmosphere has refractive or reflective effects on short-wave frequencies, and solar activity and changes in the Earth's magnetic field further impact their propagation characteristics. Weather phenomena like precipitation and thunderstorms can also cause signal attenuation or multipath propagation, affecting signal quality. Furthermore, different frequency bands exhibit different propagation behaviors in the atmosphere: low-frequency waves mainly rely on ionospheric reflection, while high-frequency waves are more affected by atmospheric humidity, temperature variations, and precipitation [2]. Therefore, the atmospheric environment has a significant impact on electromagnetic spectrum allocation. With the rapid evolution of various radio communication systems and the increasing density of radio signals, the utilization of radio frequencies has become increasingly saturated [3]. Communication protocols and signal types are diversifying, while advanced modulation technologies have made communication signals more flexible and varied. The development and application of various radio devices have led to a proliferation of numerous, dynamically overlapping radio signals in the electromagnetic space, resulting in frequent signal overlap and a progressively complex electromagnetic environment [4]. Furthermore, the advancement of software-defined radio technology has provided many devices with greater opportunities for interference and the utilization of radio spectrum resources, which can disrupt normal communication [5]. In this non-cooperative supervisory control scenario, effectively protecting one's electronic devices and systems from adversarial interference, is crucial for ensuring spectrum resource advantages [6] and supporting B5G and 6G communication [7] and in a complex electromagnetic environment [8]. In recent years, research into multi-agent technology has regained significant attention. The study of multi-agent games has gradually expanded from simple games involving just two agents, such as Go and Tic-Tac-Toe [9], to more complex domains. This includes large-scale network games like StarCraft [10], multi-robot cooperation in physical scenarios, and simulation-based combat scenarios in modern warfare contexts. In a multi-agent environment, each agent improves its strategy by interacting with the environment and receiving rewards, thereby seeking the optimal strategy for that environment. This process is known as multi-agent reinforcement learning [11]. Multi-agent reinforcement learning is a key artificial intelligence technology for solving multi-agent games. It achieves the optimal strategies for multiple agents by solving Nash equilibrium strategies in stochastic games [12]. In multi-agent reinforcement learning, the large state and joint action spaces of multiple agents lead to challenges with the explosion of search space dimensions, which restricts the effectiveness of learning strategies. In recent years, deep learning has enabled high-dimensional feature representation through the use of deep networks for function approximation and convolutional networks for feature extraction [13]. Deep reinforcement learning is a fusion of deep learning and reinforcement learning [14]. It employs deep networks for the expression and iterative updating of policies, effectively addressing the challenges of feature extraction and representation in high-dimensional spaces.