  The limited capabilities of user equipment restrict the local implementation
of computation-intensive applications. Edge computing, especially the edge
intelligence system, enables local users to offload the computation tasks to
the edge servers to reduce the computational energy consumption of user
equipment and accelerate 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 arising from
scarce wireless communication resources, we propose a semantic-aware
multi-modal task offloading system that facilitates the extraction and
offloading of semantic task information to edge servers. To cope with the
different tasks with multi-modal data, a unified quality of experience (QoE)
criterion is designed. Furthermore, a proximal policy optimization-based
multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate
the resource management for wireless communications and computation in a
distributed and low computational complexity manner. Simulation results verify
that the proposed MAPPO algorithm outperforms other reinforcement learning
algorithms and fixed schemes in terms of task execution speed and the overall
system QoE.
