Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance Using Deep Reinforcement Learning

In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while...

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Bibliographic Details
Main Authors: Eivind Meyer, Haakon Robinson, Adil Rasheed, Omer San
Format: Article
Published: IEEE 2020-01-01
Series:IEEE Access
Online Access:https://ieeexplore.ieee.org/document/9016254/