## 1 Introduction  
The significant surge in computation-intensive tasks within mobile applications has placed substantial computational burdens on devices with limited resources in recent years. Despite advancements in hardware for devices like smartphones and IoT devices, they frequently struggle to meet the demands of these resource-intensive tasks. In response to this issue, dew computing has emerged as a solution, aiming to alleviate the situation by offloading computation-intensive tasks to more potent devices, typically those nearby. In this context, \"nearby devices\" are defined as devices connected to the same local network [1, 2, 3, 4, 5]. The central premise is to transfer tasks, for example, from a smartphone to a laptop, leveraging the increased processing power of the latter. This approach not only results in faster task execution but also prevents excessive depletion of the smartphone's battery resources. While dew computing holds promising potential, its practical implementation hinges on addressing a critical challenge: the efficient job distribution across nearby devices. This study focuses on resolving the job distribution challenge within dew environments. Dew environments comprise a collection of devices interconnected within a local network. These devices exhibit variations in their attributes and capabilities, encompassing factors such as power source, battery capacity, processor/CPU core count, storage capacity, and sensory capabilities. Furthermore, users may interact with selected devices at diverse points in time. To ensure the efficient distribution of tasks within a dew environment, it becomes imperative to consider all these factors comprehensively. Current methods for distributing jobs in dew environments follow human-designed heuristics. These heuristics balance the workload among devices by following predefined rules. Some of these rules combine devices' features such as computing capability, the remaining battery level, current queued jobs, and job requirement information to select the most appropriate device to be assigned with a job. Some examples include the _Enhanced Simple Energy-Aware Scheduler (E-SEAS)_[6] and the _Batch Processing Algorithm (BPA)_[7]. _Round Robin (RR)_[8] is also considered for distributing jobs, quite frequently as a baseline method for new proposals. Unfortunately, the performance of these methods remains unknown when the network topology dynamically changes over time due to network conditions. Consequently, they lead to suboptimal decision making and the wastage of valuable resources. Exploring the benefits of considering network-related parameters as part of job distribution algorithms, we propose a reliability score that accounts for the time a device--also called a node--is connected to a dew environment. The score is integrated into previously proposed human-designed heuristics and thoroughly evaluated. The results obtained encourage us to incorporate the reliability score into the learning process of a reinforcement learning (RL) agent. Prior research [9] highlights that RL effectively accommodates dew computing environments [10]. RL is a subfield of artificial intelligence that studies how to develop agents that can learn optimal behavior by interacting with an environment. Every interaction with the environment delivers a reward signal that the agent seeks to maximize. The agent improves its current policy (mapping observations to actions) by learning from past experiences to accomplish this. Powered by deep learning, RL agents have been used to solve complex decision-making problems across different research areas, from robotics [11] to conversational agents [12] to molecule discovery [13]. Here, we propose letting an RL agent learn how to distribute jobs effectively in dew environments with nodes' mobility presence. The main contributions of our work are, then, as follows. First, we extend EdgeDewSim [7] with all necessary features to support dynamic changes in the network conditions as a consequence of nodes' mobility.