  The trend of massive connectivity pushes forward the explosive growth of end
devices. The emergence of various applications has prompted a demand for
pervasive connectivity and more efficient computing paradigms. On the other
hand, the lack of computational capacity of the end devices restricts the
implementation of the intelligent applications, and becomes a bottleneck of the
multiple access for supporting massive connectivity. Mobile cloud computing
(MCC) and mobile edge computing (MEC) techniques enable end devices to offload
local computation-intensive tasks to servers by networks. In this paper, we
consider the cloud-edge-end collaborative networks to utilize distributed
computing resources. Furthermore, we apply task-oriented semantic
communications to tackle the fast-varying channel between the end devices and
MEC servers and reduce the communication cost. To minimize long-term energy
consumption on constraints queue stability and computational delay, a
Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed
to solve the mixed integer non-linear programming (MINLP) problem. The
long-term energy consumption minimization problem is transformed into the
deterministic problem in each time frame. The DRLH framework integrates a
model-free deep reinforcement learning algorithm with a model-based
mathematical optimization algorithm to mitigate computational complexity and
leverage the scenario information, so that improving the convergence
performance. Numerical results demonstrate that the proposed DRLH framework
achieves near-optimal performance on energy consumption while stabilizing all
queues.
