The limited computation capacity of user equipments restricts the local
implementation of computation-intense applications. Edge computing, especially
the edge intelligence system enables local users to offload the computation
tasks to the edge servers for reducing the computational energy consumption of
user equipments and fast task execution. However, the limited bandwidth of
upstream channels may increase the task transmission latency and affect the
computation offloading performance. To overcome the challenge of the limited
resource of wireless communications, we adopt a semantic-aware task offloading
system, where the semantic information of tasks are extracted and offloaded to
the edge servers. Furthermore, a proximal policy optimization based multi-agent
reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource
of wireless communications and the computation, so that the resource management
can be performed distributedly and the computational complexity of the online
algorithm can be reduced.Comment: Have been accepted by IEEE ICC 202