ARLO: An asynchronous update reinforcement learning-based offloading algorithm for mobile edge computing

The processing of large volumes of data sets unprecedented demands on the computing power of devices, and it is evident that resource-constrained mobile devices struggle to satisfy the need. As a distributed computing paradigm, edge computing can release mobile devices from computation-intensive tasks, reducing the strain and improving processing efficiency. Traditional offloading methods are less adaptable and do not work in some harsh settings. We simplify the problem to binary offloading decisions in this research and offer a new Asynchronous Update Reinforcement Learning-based Offloading (ARLO) algorithm. The method employs a distributed learning strategy, with five sub-networks and a central public network. Each sub-network has the same structure, as they interact with their environment to learn and update the public network. The sub-network pulls the parameters of the central public network every once in a while. Each sub-network has an experienced pool that minimizes data correlation and is particularly successful in preventing situations where the model falls into a local optimum solution. The main reason for using asynchronous multithreading is that it allows multiple threads to learn the strategy simultaneously, making the learning process faster. At the same time, when the model is trained, five threads can run simultaneously and can handle tasks from different users. The results of simulations show that the algorithm is adaptive and can make optimized offloading decisions on time, even in a time-varying Internet environment, with a significant increase in computational efficiency compared to traditional methods and other reinforcement learning methods.