Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

16-11-2020 | Original Article

Dynamic fusion for ensemble of deep Q-network

Journal:
International Journal of Machine Learning and Cybernetics
Authors:
Patrick P. K. Chan, Meng Xiao, Xinran Qin, Natasha Kees
Important notes

Publisher's Note

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

Abstract

Ensemble reinforcement learning, which combines the decisions of a set of base agents, is proposed to enhance the decision making process and speed up training time. Many studies indicate that an ensemble model may achieve better results than a single agent because of the complement of base agents, in which the error of an agent may be corrected by others. However, the fusion method is a fundamental issue in ensemble. Currently, existing studies mainly focus on static fusion which either assumes all agents have the same ability or ignores the ones with poor average performance. This assumption causes current static fusion methods to overlook base agents with poor overall performance, but excellent results in select scenarios, which results in the ability of some agents not being fully utilized. This study aims to propose a dynamic fusion method which utilizes each base agent according to its local competence on test states. The performance of a base agent on the validation set is measured in terms of the rewards achieved by the agent in next n steps. The similarity between a validation state and a new state is quantified by Euclidian distance in the latent space and the weights of each base agent are updated according to its performance on validation states and their similarity to a new state. The experimental studies confirm that the proposed dynamic fusion method outperforms its base agents and also the static fusion methods. This is the first dynamic fusion method proposed for deep reinforcement learning, which extends the study on dynamic fusion from classification to reinforcement learning.

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.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article