  To support future 6G mobile applications, the mobile edge computing (MEC)
network needs to be jointly optimized for computing, pushing, and caching to
reduce transmission load and computation cost. To achieve this, we propose a
framework based on deep reinforcement learning that enables the dynamic
orchestration of these three activities for the MEC network. The framework can
implicitly predict user future requests using deep networks and push or cache
the appropriate content to enhance performance. To address the curse of
dimensionality resulting from considering three activities collectively, we
adopt the soft actor-critic reinforcement learning in continuous space and
design the action quantization and correction specifically to fit the discrete
optimization problem. We conduct simulations in a single-user single-server MEC
network setting and demonstrate that the proposed framework effectively
decreases both transmission load and computing cost under various
configurations of cache size and tolerable service delay.
