期刊
OCEAN ENGINEERING
卷 235, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.oceaneng.2021.109433
关键词
Dynamic Positioning; Deep Reinforcement Learning; Proximal policy optimization; Reward shaping
资金
- Research Council of Norway through the Centre of Excellence funding scheme [223254]
This paper presents the implementation and performance testing of a Deep Reinforcement Learning based control scheme for Dynamic Positioning of a marine surface vessel, demonstrating good positioning performance and energy efficiency through simulations and model scale sea trials.
This paper demonstrates the implementation and performance testing of a Deep Reinforcement Learning based control scheme used for Dynamic Positioning of a marine surface vessel. The control scheme encapsulated motion control and control allocation by using a neural network, which was trained on a digital twin without having any prior knowledge of the system dynamics, using the Proximal Policy Optimization learning algorithm. By using a multivariate Gaussian reward function for rewarding small errors between the vessel and the various setpoints, while encouraging small actuator outputs, the proposed Deep Reinforcement Learning based control scheme showed good positioning performance while being energy efficient. Both simulations and model scale sea trials were carried out to demonstrate performance compared to traditional methods, and to evaluate the ability of neural networks trained in simulation to perform on real life systems.
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