  Caching networks are designed to reduce traffic load at backhaul links, by
serving demands from edge-nodes. In the past decades, many studies have been
done to address the caching problem. However, in practice, finding an optimal
caching policy is still challenging due to dynamicity of traffic and
scalability caused by complex impact of caching strategy chosen by each
individual cache on other parts of network. In this paper, we focus on cache
placement to optimize the performance metrics such as hit ratio in cooperative
large-scale caching networks. Our proposed solution, cooperative multi-agent
based cache placement (CoM-Cache) is based on multi-agent reinforcement
learning framework and can seamlessly track the content popularity dynamics in
an on-line fashion. CoM-Cache is enable to solve the problems over a spectrum
from isolated to interconnected caches and is designed flexibly to fit any
caching networks. To deal with dimensionality issue, CoM-Cache exploits the
property of locality of interactions among caches. The experimental results
report CoM-Cache outperforms base-line schemes, however at the expense of
reasonable additional complexity.
