Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle

Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle

Autonomous underwater vehicle (AUV) plays an increasingly important role in ocean exploration. Existing AUVs are usually not fully autonomous and generally limited to pre-planning or pre-programming tasks. Reinforcement learning (RL) and deep reinforcement learning have been introduced into the AUV...

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Bibliographic Details
Main Authors: Qilei Zhang, Jinying Lin, Qixin Sha, Bo He, Guangliang Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Autonomous underwater vehicle
interactive reinforcement learning
deep Q network
path following
Online Access:https://ieeexplore.ieee.org/document/8976170/

Internet

https://ieeexplore.ieee.org/document/8976170/

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