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. 

本文针对大规模天地融合网络中的内容缓存问题展开研究，聚焦未来大规模遥感星座与通信卫星网络的融合场景。本研究采用深度强化学习技术，以最小化长期平均内容传输时延为目标。为解决单智能体算法固有的收敛难题，我们提出对智能遥感卫星进行聚类，每个聚类集群由一个智能体作为牵头节点。基于该模型的特性，我们对多智能体近端策略优化（multi-agent proximal policy optimization, MAPPO）算法进行改进，引入长短期记忆网络（long short-term memory, LSTM）以捕捉不同智能体的状态信息在时域上的相关性。仿真结果表明，所提LSTM-MAPPO算法优于各类基准算法，展现出更快的收敛速度与更低的标准差。