In this paper, we aim to explore the use of uplink semantic communications
with the assistance of UAV in order to improve data collection effiicency for
metaverse users in remote areas. To reduce the time for uplink data collection
while balancing the trade-off between reconstruction quality and computational
energy cost, we propose a hybrid action reinforcement learning (RL) framework
to make decisions on semantic model scale, channel allocation, transmission
power, and UAV trajectory. The variables are classified into discrete type and
continuous type, which are optimized by two different RL agents to generate the
combined action. Simulation results indicate that the proposed hybrid action
reinforcement learning framework can effectively improve the efficiency of
uplink semantic data collection under different parameter settings and
outperforms the benchmark scenarios.

本研究旨在探索使用助理无人机的上行语义通信，以提高偏远地区元宇宙用户的数据采集效率。为了在重建质量和计算能量成本之间平衡的同时减少上行数据采集时间，我们提出了一种混合动作强化学习框架，用于在语义模型规模、信道分配、传输功率和无人机轨迹上做出决策。变量分为离散类型和连续类型，并由两个不同的强化学习代理进行优化以生成合并的动作。模拟结果表明，所提出的混合动作强化学习框架可以在不同的参数设置下有效提高上行语义数据采集的效率，并优于基准情况。