Multi-UAV Assisted Offloading Optimization: A Game Combined Reinforcement Learning Approach

Although unmanned aerial vehicles (UAVs) have attracted much attention by providing aerial relays to massive ground users (GUs) for tasks offloading, there still exist several issues, such as the unbalance of tasks size and trajectory optimization related to energy efficiency and obstacles avoidance. The letter models the multi-UAV assisted offloading system as two separate problems optimized by a potential game combined reinforcement learning algorithm, i.e., potential game for service assignment, and deep deterministic policy gradient (DDPG) for trajectory planning. The former largely reduces the convergence time, and the latter can search the best action in a continuous domain. The numerical results show that the proposed approach has great advantages in minimizing offloading delay, enhancing energy efficiency and avoiding obstacles.