We study the problem of decentralized task offloading and load-balancing in a
dense network with numerous devices and a set of edge servers. Solving this
problem optimally is complicated due to the unknown network information and
random task sizes. The shared network resources also influence the users'
decisions and resource distribution. Our solution combines the mean field
multi-agent multi-armed bandit (MAB) game with a load-balancing technique that
adjusts the servers' rewards to achieve a target population profile despite the
distributed user decision-making. Numerical results demonstrate the efficacy of
our approach and the convergence to the target load distribution.

我们研究了在一个拥有大量设备和一组边缘服务器的稠密网络中的分散任务卸载和负载平衡问题。通过将均值场多智能体多臂赌博（MAB）游戏与一种负载平衡技术相结合，调整服务器的奖励以实现目标人口分布，解决了由于未知网络信息和随机任务大小而导致的复杂优化问题。数值结果证明了我们方法的有效性和对目标负载分布的收敛。