## 2 Related Work
Additionally, it does not explain how tasks will be treated if they do not arrive at the same time on all mobile devices. In [12], the authors introduce the concept of a candidate network. In this model, instead of training only one DQL network, \(n\) DQL networks are trained, where some are trained after every \(C\) iterations while others are trained after every iteration. At each step, the network with the smallest \(Q\) value is selected as the candidate network to be used for action selection. Regarding the optimization criteria, it uses a combination of power consumption, cost, and load balancing. The cost is a dynamic function of the amount of processing resources remaining at an edge device at the moment. In [13], each mobile device can connect to multiple edge devices and when a task is offloaded to an edge device, it is either executed or dropped. Each task has a deadline by which it needs to be fully executed; if an edge device does not have the resources to execute a task on time, the task will be dropped. With regard to the cost function, if a task is executed, the cost function will be a function of the time delay, and if a task is dropped, a constant cost value is considered. The optimization problem is to minimize the total cost of all tasks. To do this, a deep Q-learning model with LSTM [14] as mid-layer is employed. In this model, mobile devices choose an edge device to train a neural network instead of training it themselves. The reason for this is to alleviate the load on mobile devices, as edge devices have more processing power. However, a separate model is trained on the edge device for each mobile device using the experiences of that mobile device. This is a major disadvantage, as the edge device will need to train a relatively large number of neural networks which can use significant processing resources of the edge device. Additionally, if the mobile device is disconnected from the chosen edge device, a new neural network on another edge device must be trained and the previous experiences will be lost. The authors of [15] also employed LTSM [14] mid-layers within their deep Q-network, which allows the network to memorize more information and reduce training time. They, however, introduced three types of MECs for task offloading: base stations (BS) with more processing power, but which are usually farther from mobile devices, road side units that have less processing power but are usually closer to mobile devices, and parked idle vehicles, which can be thought of as other mobile devices not currently in use. This architecture allows the system to take advantage of the mobile devices with no workload to assist other mobile devices. Similarly, the authors in [16] introduced a pairing scheme to pair resource-constrained mobile devices with idle mobile devices to balance the load between them. They, however, built their model with the assumption that the number of idle devices is always greater than the number of resource-constrained devices, which is not always the case. In [17], mobile devices do not perform local execution and it is assumed that each mobile device is within the coverage of one and only one unmanned aerial vehicle (UAV), to which it offloads its tasks. This requires that the area of coverage for UAVs not overlap. Additionally, UAVs may be connected to a number of edge clouds (ECs) to further offload some of the tasks received from mobile devices. In a sense, in this system, UAVs can be thought of as traditional mobile devices with limited processing and energy capacity, and ECs as traditional MECs. Even though each UAV trains its own model for task offloading, a multi-agent TD3 algorithm [18] is employed to promote cooperation between UAVs instead of each UAV maximizing its own reward. On the contrary, [PERSON] et al. in [19] argue that it is fairer for each mobile device to minimize the workload and energy consumption for itself, as mobile devices are owned by different users.