## 1 Introduction  
According to the fact that the 5G era has been realized based on networking technology innovation, many new computing and communications paradigms are introduced such as millimeter-wave communications, hyper-dense networks, and device-to-device proximal networking [1; 2; 3]. Among them, mobile edge computing (MEC) is one of the major technologies for realizing data/computing distribution in order to improve cellular network performance [4]. Based on the benefits of MEC technologies such as data rate improvement and quality enhancement, mobile cellular users can enjoy high quality, seamless, and real-time communication networking services. Together with the MEC technologies, many networked components are also of interest such as cloud servers and connected devices/vehicles. As the number of connected devices/vehicles increases, the amount of data transmitted to the MEC is also rapidly increasing. This obviously introduces serious network limitations such as data processing performance limits, storage capacity limits, and the battery use of terminal devices. Under the consideration of these limitations and problems, the use of cloud computing server is more efficient to deal with big data (gathered from connected vehicles/devices via MEC edges) than the local computing on terminals. On the other hand, for any cases where the data cannot be handled in the cloud servers due to delay requirements and security reasons, MEC devices should be able to handle or process the data from the connected vehicles/devices. Therefore, we can observe the trade-off between cloud computing servers and MEC servers (local edge computing servers). In summary, cloud servers have more power and centralized computing benefitscomparing to MEC servers, whereas, it may have limitations when we have delay requirements and security requirements/regulations. Figure 1 shows the combined architecture of local edge computing and cloud computing. As the transmission from connected vehicles/devices (i.e., data upload and download) delay goes down, it is much more suitable for real-time processing. It enables real-time data processing and transmission using high-quality data in real-time broadcasting or video streaming. Therefore, it is essential to design an algorithm which can handle the data transmission scheduling (i.e., offloading decision-making, i.e., centralized vs. local computing). Note that the data from connected vehicles/devices will be handled in cloud computing servers when offloading decision is made, whereas the MEC servers will handle the data when the offloading is not preferred. In order to handle this problem, many research contributions nowadays have been considering deep learning and machine learning based methodologies. Among them, this paper considers reinforcement learning based approaches because this given problem is for stochastic sequential _offloading_ decision-making. Among a lot of deep reinforcement learning (DRL) methodologies such as Q-learning, Markov decision process (MDP) [5], deep Q-network (DQN), and deep deterministic policy gradient (DDPG) [6; 7], this paper designs a sequential offloading decision-making algorithm based on DQN. The reason why this paper considers DQN is that it is the function approximation of Q-learning using deep neural network (DNN) in order to take care of large-scale problem setting. The proposed sequential offloading decision-making algorithm inspired by DQN aims at the maximization the cumulative summation of the rewards for sequential offloading actions (centralized vs. local computing). Therefore, it is obvious that the reinforcement learning is a way to proceed optimal learning even in a dynamic environment under uncertainty. The reinforcement learning is applied to a variety of environments such as artificial intelligence robotics development, automatic systems, and self-driving technology development.