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Attention-Based Meta-Reinforcement Learning for Tracking Control of AUV With Time-Varying Dynamics.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Nov2022, Vol. 33 Issue 11, p6388-6401, 14p
- Publication Year :
- 2022
-
Abstract
- Reinforcement learning (RL) is a promising technique for designing a model-free controller by interacting with the environment. Several researchers have applied RL to autonomous underwater vehicles (AUVs) for motion control, such as trajectory tracking. However, the existing RL-based controller usually assumes that the unknown AUV dynamics keep invariant during the operation period, limiting its further application in the complex underwater environment. In this article, a novel meta-RL-based control scheme is proposed for trajectory tracking control of AUV in the presence of unknown and time-varying dynamics. To this end, we divide the tracking task for AUV with time-varying dynamics into multiple specific tasks with fixed time-varying dynamics, to which we apply meta-RL for training to distill the general control policy. The obtained control policy can transfer to the testing phase with high adaptability. Inspired by the line-of-sight (LOS) tracking rule, we formulate each specific task as a Markov decision process (MDP) with a well-designed state and reward function. Furthermore, a novel policy network with an attention module is proposed to extract the hidden information of AUV dynamics. The simulation environment with time-varying dynamics is established, and the simulation results reveal the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 33
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 160690170
- Full Text :
- https://doi.org/10.1109/TNNLS.2021.3079148