## 2 Related Work  
The literature around task offloading varies considerably with regard to the network model, the optimization criteria, and the methods used for optimization. In [5], all connections are wireless, even the ones between edge devices. Additionally, each mobile device is connected to at most one edge device, while edge devices can be connected to multiple edge devices. In this model, 100 mobile devices are randomly positioned in a plane, while four edge devices are positioned on four corners of a rectangle. Each edge device in this model can further offload a task to another edge device as long as the task was not received from an edge device. For the optimization criteria, they consider a combination of task response delay and energy consumption of mobile devices. Two methods are used for choosing the tasks to be offloaded: the mathematical programming and deep Q-learning [6] methods. It demonstrates that the deep Q-learning method can achieve results with similar performance as the mathematical programming solution with the advantage of being an online machine-learning method. [PERSON] et al. in [7] propose a deep reinforcement model for vehicular mobile devices. In their work, a mobile device can have a number of applications that, in turn, produce tasks with different quality of service (QoS) requirements. An acceptance criteria is then introduced, which indicates whether a task can be accepted by an edge device considering its QoS requirements. For the optimization criteria, the aim of a solution is to maximize the percentage of tasks that are accepted by edge devices. One drawback of their deep reinforcement model is that all the decisions are made by a central server and the states in the trained deep reinforcement model also change size relative to the number of edge devices in the system. This makes the system inflexible and unscalable, as the system cannot have too many edge devices and the model should be retrained each time an edge device is added or removed. In [8], there are three types of devices: wireless users (WU), U-MEC, and F-MEC. In their model, tasks arrive at WUs, which are responsible for making a decision to offload the task to a U-MEC or a F-MEC. U-MECs are unmanned vehicle-assisted servers, which have limited power and processing capacity but are generally closer to WUs and can move around closer to WUs. On the other hand, F-MECs have a fixed location, but no power restrictions and more processing power. For the optimization criteria, they used a combination of total delay, energy consumption of WUs, and bandwidth of MEC devices. For the optimization model, they used SARSA [9] and dueling deep Q-learning [10] models. SARSA is a reinforcement learning method in which the model is updated after each step when a reward for a task is obtained. In a dueling deep Q-learning model, two networks are used to estimate the value of the current state and the advantage of a chosen action. They show that, generally, the dueling deep Q-learning model performs better than SARSA. [PERSON] et al. [11] used a game theory model for the task offloading problem. They considered a model with one fixed MEC and one flying MEC as edge devices to serve mobile users on the ground. In their model, all mobile users are connected to both fixed and flying MECs and a decision to offload a task is made collectively. At each step, each mobile device advertises the best decision it can take to minimize a linear combination of the delay and power consumption costs for the current task. After all mobile devices have made their advertisements, the mobile device with action for which the total cost function is minimized \"wins\" and its decision is recorded on a list. This process is repeated until the list does not change. Their report, however, did not take into account the delay introduced by the execution of this game theoretic algorithm, as it can take several repetitions until the list comes to an equilibrium.