On the Convergence of a Reinforcement Learning Process to a Generalized Energy-Optimal Guidance Policy for Unmanned Underwater Vehicles

We demonstrate that an energy-minimizing guid-ance system for unmanned underwater vehicles-trained by deep reinforcement learning (RL) on ocean current profiles exhibiting time-stationary random variation in direction and magnitude as a function of depth-executes a distance-conditional explore-explo...

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Veröffentlicht in:	Oceans (New York. Online) S. 1 - 10
Hauptverfasser:	Greeley, Brian, Brandman, Jeremy, Book, Jeffrey, Barron, Charlie, Landry, Blake, Olson, Colin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 23.09.2024
Schlagworte:	Dead reckoning Deep reinforcement learning guidance algorithms Meters neural networks Oceans proximal policy optimization reinforcement learning Sea measurements Time measurement Training Trajectory Uncertainty unmanned underwater vehicles Upper bound
ISSN:	2996-1882
Online-Zugang:	Volltext
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