  Mobile edge computing (MEC) is regarded as a promising wireless access
architecture to alleviate the intensive computation burden at resource limited
mobile terminals (MTs). Allowing the MTs to offload partial tasks to MEC
servers could significantly decrease task processing delay. In this study, to
minimize the processing delay for a multi-user MEC system, we jointly optimize
the local content splitting ratio, the transmission/computation power
allocation, and the MEC server selection under a dynamic environment with
time-varying task arrivals and wireless channels. The reinforcement learning
(RL) technique is utilized to deal with the considered problem. Two deep RL
strategies, that is, deep Q-learning network (DQN) and deep deterministic
policy gradient (DDPG), are proposed to efficiently learn the offloading
policies adaptively. The proposed DQN strategy takes the MEC selection as a
unique action while using convex optimization approach to obtain the remaining
variables. And the DDPG strategy takes all dynamic variables as actions.
Numerical results demonstrates that both proposed strategies perform better
than existing schemes. And the DDPG strategy is superior to the DQN strategy as
it can learn all variables online although it requires relatively large
complexity.
