  We consider a system comprising a file library and a network with a server
and multiple users equipped with cache memories. The system operates in two
phases: a prefetching phase, where users load their caches with parts of
contents from the library, and a delivery phase, where users request files from
the library and the server needs to send the uncached parts of the requested
files to the users. For the case where the users' caches are arbitrarily
loaded, we propose an algorithm based on deep reinforcement learning to
minimize the delay of delivering requested contents to the users in the
delivery phase. Simulation results demonstrate that our proposed deep
reinforcement learning agent learns a coded delivery strategy for sending the
requests to the users, which slightly outperforms the state-of-the-art
performance in terms of delivery delay, while drastically reducing the
computational complexity.
