## 3 Related Work
In this problem setup, previous works have shown that RL agents can reduce the execution time in distributed systems [31] and avoid overloading (and deadlocking) cloud servers [32]. Furthermore, Ref. [33] showed that deep RL can helpschedule tasks in large-scale cloud service providers, surpassing traditional methods in terms of energy cost and reject rates. As for edge computing, the task scheduling problem is similar. The edge server receives tasks and has to decide whether to send those tasks to a nearby server or the cloud. Several works have explored how to distribute jobs in edge computing using deep learning techniques like convolutional neural networks or reinforcement learning according to different performance metrics. These metrics include reducing computing times [34, 35], energy consumption [36], latency [37, 38], task failure rate [39], or a combination of the previous [40, 41, 42]. We note that in edge computing, the action space is usually limited. For instance, sometimes the agent can only decide whether to send (or not) a job to the cloud [41]. In contrast, the action space in a dew environment typically ranges from tens to hundreds (i.e., one action per device), making the decision-making problem considerably harder. RL has been increasingly utilized for job scheduling in environments that are closely related to one another, such as cloud computing [31, 32, 33] and edge computing [34, 36, 37, 38, 39, 40, 41, 42]. With emerging studies in dew computing, RL has been applied in cloud and edge computing for job scheduling. For instance, in Ref. [43], they propose a vehicular dew computing architecture using RL for content delivery optimization. Similarly, in Ref. [44], they suggest a dew computing-based vehicular edge caching architecture to enhance vehicle stability and high-definition map acquisition. In Ref. [45], they introduce dew quantum machine learning (DewQML) to improve decision making and reduce latency in dew computing environments. In Ref. [46], they present a dew computing-based microservice execution (DoME) scheme using RL to minimize service delay and optimize costs in mobile edge computing. Additionally, research shows deep RL agents can effectively offload jobs in dew computing environments. Finally, in the context of job offloading in dew computing environments, prior research demonstrates that deep RL agents can offload jobs more effectively than traditional state-of-the-art heuristic methods, even when faced with previously unseen scenarios. The study conducted by [9] empirically proves that the agent learns to generalize in network environments that lack dynamic components when continuously exposed to new situations. This means that the agent can appropriately distribute sequences of jobs that arrive in patterns and sizes not encountered during training. Furthermore, the agent can learn to effectively distribute jobs in fixed dew environments, significantly outperforming state-of-the-art heuristics regarding the number of instructions executed per second. This highlights the potential of RL in enhancing the efficiency and adaptability of job scheduling in dew computing environments, paving the way for more responsive and robust computing systems [9]. In the same line of Ref. [9], there are three main differences between our work and the existing work on RL with cloud and edge computing. We address a different problem. Distributing jobs in a dew environment has challenges that do not typically arise in cloud or edge computing. For instance, in dew computing, some devices might run out of battery, or users might start interacting with them. When the network conditions are dynamic, the availability of the devices cannot be assured. Taking these elements into account is a nontrivial task. Second, we thoroughly examine the transfer learning capabilities exhibited by the policies acquired by the reinforcement learning agent.