Digital Twin Supervised Reinforcement Learning Framework for Autonomous Underwater Navigation

This article presents a research paper on a novel approach to autonomous underwater navigation using a digital twin and reinforcement learning. The use of a digital twin allows for safe and efficient training of the reinforcement learning agent. The framework likely addresses challenges related to underwater environments such as limited visibility, currents, and communication constraints. The paper's contribution lies in the integration of these technologies for improved underwater navigation.

• •Focuses on autonomous underwater navigation.
• •Employs a digital twin for training.
• •Utilizes reinforcement learning.
• •Addresses challenges of underwater environments.