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...
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| Veröffentlicht in: | IEEE communications letters 2021-08, Vol.25 (8), p.2629-2633 |
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| creator | Gao, Ang Wang, Qi Chen, Kaiyue Liang, Wei |
| description | 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. |
| doi_str_mv | 10.1109/LCOMM.2021.3078469 |
| format | Article |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Convergence DDPG Delays DRL Energy consumption Energy conversion efficiency Energy efficiency Games Machine learning Obstacle avoidance Offloading potential game Relays Task analysis Trajectory optimization Trajectory planning Unmanned aerial vehicles |
| title | Multi-UAV Assisted Offloading Optimization: A Game Combined Reinforcement Learning Approach |
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