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ORIGINAL RESEARCH article
Front. Neurorobot.
Volume 18 - 2024 |
doi: 10.3389/fnbot.2024.1466571
Noisy Dueling Double Deep Q-Network Algorithm for Autonomous Underwater Vehicle Path Planning
Provisionally accepted- 1 School of Meteorology and Oceanography, National University of Defense Technology,, Changsha, Anhui Province, China
- 2 School of Mathematics and Statistics, Changsha University of Science and Technology,, Changsha, Anhui Province, China
- 3 College of Science, Hunan University of Science and Engineering, Yongzhou, China
How to improve the success rate of autonomous underwater vehicle (AUV) path planning and reduce travel time as much as possible is a very challenging and crucial problem in the practical applications of AUV in the complex ocean current environment. Traditional reinforcement learning algorithms lack exploration of the environment, and the strategies learned by the agent may not generalize well to other different environments. To address these challenges, we propose a novel AUV path planning algorithm named the Noisy Dueling Double Deep Q-Network (ND3QN) algorithm by modifying the reward function and introducing a noisy network, which generalizes the traditional D3QN algorithm. Compared with the classical algorithm (e.g., Rapidly-exploring Random Trees Star (RRT*), DQN, and D3QN), with simulation experiments conducted in realistic terrain and ocean currents, the proposed ND3QN algorithm demonstrates the outstanding characteristics of a higher success rate of AUV path planning, shorter travel time, and smoother paths.
Keywords: AUV, path planning, deep reinforcement learning, ND3QN, noisy network
Received: 18 Jul 2024; Accepted: 23 Sep 2024.
Copyright: © 2024 Liao, Li, Huang, Zhao and Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Le Li, School of Meteorology and Oceanography, National University of Defense Technology,, Changsha, Anhui Province, China
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