## 2 Related Work
In the study of collaborative caching algorithms for dynamic network environments, multi-agent deep reinforcement learning (MARL) has demonstrated significant potential. The authors in [26] proposed the C-MAAC algorithm based on the multi-agent actor-critic (MAAC) framework, which innovatively formulates the collaborative caching problem as a multi-agent Markov decision process (MDP). By adopting a centralized training with decentralized execution (CTDE) framework, this method effectively addresses the instability in training environments caused by decentralized multi-agent decision making. The introduction of a Gumbel-Softmax sampler to generate differentiable action spaces significantly enhances the stability of the training process. Experimental results demonstrate that, under dynamic content popularity scenarios, C-MAAC achieves notable improvements in cache hit rates compared to traditional methods (LFU, LRU) and deep reinforcement learning benchmarks (I-DDPG and D-DQN). In contrast, [27] approached the problem from the perspective of partially observable environments, proposing a multi-agent deep reinforcement learning framework based on the partially observable Markov decision process (POMDP) modeling. Their algorithm integrates a communication module to enable the interactive sharing of state-action information among base stations, employs variational recurrent neural networks for content request state estimation, and innovatively incorporates LSTM to model the temporal characteristics of user access patterns. Experimental results indicate that, compared to existing methods such as LRU and MAAC, this algorithm achieves significant enhancements in both caching rewards and hit rates. [28] addresses the collaborative edge caching problem in LEO satellite networks, aiming to reduce traffic congestion and request latency by caching popular content. It proposes the SEC-MAPPO method, which is based on the multi-agent proximal policy optimization (MAPPO) algorithm and models the problem as a POMDP to tackle the challenges of a brief satellite overhead time and dynamic network topology. The experimental results indicate that SEC-MAPPO significantly outperforms baseline algorithms like RANDOM, LFU, LRU, and deep Q-learning network (DQN) in reducing the average video request latency, demonstrating its effectiveness in practical LEO satellite network scenarios. Current research on the remote sensing data push in satellite-terrestrial integrated networks centers on three pivotal dimensions: user demand modeling, dynamic network adaptation, and collaborative distribution mechanisms. While significant progress has been made in each area, critical challenges persist. In user demand modeling, innovative approaches such as multi-attribute decision frameworks leveraging quadruple user profiling, spatio-temporal topic models like STPT and STET, and advanced deep learning architectures including multi-modal knowledge graphs and attention fusion networks have demonstrably enhanced demand prediction accuracy [29; 30]. However, these methodologies exhibit notable limitations in capturing the intricate heterogeneity of spatio-temporal correlations and cross-modal feature interactions inherent in remote sensing data, often relying on overly coarse spatial representations and static feature weighting schemes that fail to adequately resolve complex semantic patterns. Transitioning to dynamic network adaptation, existing strategies employing density-based partitioning and spatio-temporal feature tagging attempt to mitigate the challenges posed by intermittent connectivity and resource constraints in hybrid networks. Many of these solutions remain anchored to static network assumptions or rely on approximation algorithms, rendering them ill-equipped to handle the high-frequency topological variations characteristic of large-scale LEO constellations. Furthermore, the precision of coupling models linking remote sensing data semantics with dynamic network states requires substantial refinement.