dataset for collaborative content caching algorithm

In this letter, we investigate the content caching problem within large-scale integrated satellite-terrestrial net works, focusing on a fusion scenario of future large-scale remote sensing constellations and communication satellite networks. Our investigation relies on deep reinforcement learning techniques aimed at minimizing the long-term average content delivery delay. To address the inherent challenge of convergence in single agent algorithms, we propose clustering intelligent remote sensing satellites, with each cluster headed by an intelligent agent. Based on the characteristics of the model, we modify the multi-agent proximal policy optimization (MAPPO) algorithm by integrating long short-term memory (LSTM) to capture the correlation of the state information of different agents in the time domain. Simula tion results show that the proposed LSTM-MAPPO outperforms the benchmarks, exhibiting faster convergence speed and lower standard deviation.