• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Høgskulen på Vestlandet
  • Fakultet for ingeniør- og naturvitskap / Faculty of Engineering and Science
  • Institutt for datateknologi, elektroteknologi og realfag
  • View Item
  •   Home
  • Høgskulen på Vestlandet
  • Fakultet for ingeniør- og naturvitskap / Faculty of Engineering and Science
  • Institutt for datateknologi, elektroteknologi og realfag
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

AUV Pipeline Following using Reinforcement Learning

Fjerdingen, Sigurd Aksnes; Kyrkjebø, Erik; Transeth, Aksel Andreas
Chapter
Thumbnail
View/Open
SINTEF+S16087.pdf (161.4Kb)
URI
http://hdl.handle.net/11250/2449193
Date
2010
Metadata
Show full item record
Collections
  • Institutt for datateknologi, elektroteknologi og realfag [1300]
Abstract
This paper analyzes the application of several reinforcement learning techniques for continuous state and action spaces to pipeline following for an autonomous underwater vehicle (AUV). Continuous space SARSA is compared to the actor-critic CACLA algorithm, and is also extended into a supervised reinforcement learning architecture. A novel exploration method using the skew-normal stochastic distribution is proposed, and evidence towards advantages in the case of tabula rasa exploration is presented. Results are validated on a realistic simulator of the AUV, and confirm the applicability of reinforcement learning to optimize pipeline following behavior.

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit