Joint caching and transmission optimization problem is challenging due to the deep coupling between decisions. This paper proposes an iterative distributed multi-agent learning approach to jointly optimize caching and transmission. The goal of this approach is to minimize the total transmission delay of all users. In this iterative approach, each iteration includes caching optimization and transmission optimization. A multi-agent reinforcement learning (MARL)-based caching network is developed to cache popular tasks, such as answering which files to evict from the cache and which files to storage. Based on the cached files of the caching network, the transmission network transmits cached files for users by single transmission (ST) or joint transmission (JT) with multi-agent Bayesian learning automaton (MABLA) method. And then users access the edge servers with the minimum transmission delay. The experimental results demonstrate the performance of the proposed multi-agent learning approach.

翻译：联合缓存和传输优化问题具有挑战性,因为决定之间有着深刻的结合。本文件建议采用迭代分布式多试剂学习方法,共同优化缓存和传输。这一方法的目标是最大限度地减少所有用户的完全传输延迟。在这种迭代方法中,每种迭代方法包括缓存优化和传输优化。以多试剂强化学习(MARL)为基础的缓存网络是为了缓存流行任务,例如回答哪些文件要从缓存中取出,哪些文件要储存。根据缓存网络的缓存文件,传输网络通过一次性传输(ST)或联合传输(JT)方法为用户传输缓存文件。然后,用户在最小传输延迟的情况下访问边缘服务器。实验结果显示了拟议的多试剂学习方法的绩效。