Name
Title | ||
|---|---|---|
Uav Maneuvering Target Tracking In Uncertain Environments Based On Deep Reinforcement Learning And Meta-Learning |
Abstract | ||
|---|---|---|
This paper combines deep reinforcement learning (DRL) with meta-learning and proposes a novel approach, named meta twin delayed deep deterministic policy gradient (Meta-TD3), to realize the control of unmanned aerial vehicle (UAV), allowing a UAV to quickly track a target in an environment where the motion of a target is uncertain. This approach can be applied to a variety of scenarios, such as wildlife protection, emergency aid, and remote sensing. We consider a multi-task experience replay buffer to provide data for the multi-task learning of the DRL algorithm, and we combine meta-learning to develop a multi-task reinforcement learning update method to ensure the generalization capability of reinforcement learning. Compared with the state-of-the-art algorithms, namely the deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3), experimental results show that the Meta-TD3 algorithm has achieved a great improvement in terms of both convergence value and convergence rate. In a UAV target tracking problem, Meta-TD3 only requires a few steps to train to enable a UAV to adapt quickly to a new target movement mode more and maintain a better tracking effectiveness. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12223789 | REMOTE SENSING |
Keywords | DocType | Volume |
UAV, maneuvering target tracking, deep reinforcement learning, meta-learning, multi-tasks | Journal | 12 |
Issue | Citations | PageRank |
22 | 2 | 0.50 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Li | 1 | 578 | 45.93 |
| Zhigang Gan | 2 | 2 | 0.84 |
| Da-Qing Chen | 3 | 2 | 0.84 |
| Dyachenko Sergey Aleksandrovich | 4 | 2 | 0.50 |