  In mobile edge computing systems, an edge node may have a high load when a
large number of mobile devices offload their tasks to it. Those offloaded tasks
may experience large processing delay or even be dropped when their deadlines
expire. Due to the uncertain load dynamics at the edge nodes, it is challenging
for each device to determine its offloading decision (i.e., whether to offload
or not, and which edge node it should offload its task to) in a decentralized
manner. In this work, we consider non-divisible and delay-sensitive tasks as
well as edge load dynamics, and formulate a task offloading problem to minimize
the expected long-term cost. We propose a model-free deep reinforcement
learning-based distributed algorithm, where each device can determine its
offloading decision without knowing the task models and offloading decision of
other devices. To improve the estimation of the long-term cost in the
algorithm, we incorporate the long short-term memory (LSTM), dueling deep
Q-network (DQN), and double-DQN techniques. Simulation results with 50 mobile
devices and five edge nodes show that the proposed algorithm can reduce the
ratio of dropped tasks and average task delay by 86.4%-95.4% and 18.0%-30.1%,
respectively, when compared with several existing algorithms.
