  Emerging applications in healthcare, autonomous vehicles, and wearable
assistance require interactive and low-latency data analysis services.
Unfortunately, cloud-centric architectures cannot fulfill the low-latency
demands of these applications, as user devices are often distant from cloud
data centers. Edge computing aims to reduce the latency by enabling processing
tasks to be offloaded to resources located at the network's edge. However,
determining which tasks must be offloaded to edge servers to reduce the latency
of application requests is not trivial, especially if the tasks present
dependencies. This paper proposes a DRL approach called TPTO, which leverages
Transformer Networks and PPO to offload dependent tasks of IoT applications in
edge computing. We consider users with various preferences, where devices can
offload computation to an edge server via wireless channels. Performance
evaluation results demonstrate that under fat application graphs, TPTO is more
effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by
reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO
presents a training time approximately 2.5 times faster than an existing DRL
approach.
