Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

03-02-2020 | Original Research | Issue 2/2020

Intelligent Service Robotics 2/2020

Reinforcement learning path planning algorithm based on obstacle area expansion strategy

Journal:
Intelligent Service Robotics > Issue 2/2020
Authors:
Haiyang Chen, Yebiao Ji, Longhui Niu
Important notes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

We improve the traditional Q(\( \lambda \))-learning algorithm by adding the obstacle area expansion strategy. The new algorithm is named OAE-Q(\( \lambda \))-learning and applied to the path planning in the complex environment. The contributions of OAE-Q(\( \lambda \))-learning are as follows: (1) It expands the concave obstacle area in the environment to avoid repeated invalid actions when the agent falls into the obstacle area. (2) It removes the extended obstacle area, which reduces the learning state space and accelerates the convergence speed of the algorithm. Extensive experimental results validate the effectiveness and feasibility of OAE-Q(\( \lambda \))-learning on the path planning in complex environments.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 2/2020

Intelligent Service Robotics 2/2020 Go to the issue

Editorial

Editorial