  The growing demand on high-quality and low-latency multimedia services has
led to much interest in edge caching techniques. Motivated by this, we in this
paper consider edge caching at the base stations with unknown content
popularity distributions. To solve the dynamic control problem of making
caching decisions, we propose a deep actor-critic reinforcement learning based
multi-agent framework with the aim to minimize the overall average transmission
delay. To evaluate the proposed framework, we compare the learning-based
performance with three other caching policies, namely least recently used
(LRU), least frequently used (LFU), and first-in-first-out (FIFO) policies.
Through simulation results, performance improvements of the proposed framework
over these three caching algorithms have been identified and its superior
ability to adapt to varying environments is demonstrated.
