  An online resource scheduling framework is proposed for minimizing the sum of
weighted task latency for all the Internet of things (IoT) users, by optimizing
offloading decision, transmission power and resource allocation in the
large-scale mobile edge computing (MEC) system. Towards this end, a deep
reinforcement learning (DRL) based solution is proposed, which includes the
following components. Firstly, a related and regularized stacked auto encoder
(2r-SAE) with unsupervised learning is applied to perform data compression and
representation for high dimensional channel quality information (CQI) data,
which can reduce the state space for DRL. Secondly, we present an adaptive
simulated annealing based approach (ASA) as the action search method of DRL, in
which an adaptive h-mutation is used to guide the search direction and an
adaptive iteration is proposed to enhance the search efficiency during the DRL
process. Thirdly, a preserved and prioritized experience replay (2p-ER) is
introduced to assist the DRL to train the policy network and find the optimal
offloading policy. Numerical results are provided to demonstrate that the
proposed algorithm can achieve near-optimal performance while significantly
decreasing the computational time compared with existing benchmarks.
