UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning
Li, B., Gan, Z., Chen, D. and Aleksandrovich, D.S. (2020). UAV Maneuvering Target Tracking in Uncertain Environments based on Deep Reinforcement Learning and Meta-learning. Remote Sensing.
|Authors||Li, B., Gan, Z., Chen, D. and Aleksandrovich, D.S.|
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 multi-tasks experience replay buffer to provide data for multi-tasks learning of 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, 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.
|Keywords||UAV; Maneuvering target tracking; Deep reinforcement learning; meta-learning; multi-tasks|
|Publication process dates|
|Accepted||16 Nov 2020|
|Deposited||17 Nov 2020|
|Accepted author manuscript|
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