A multiagent fuzzy policy reinforcement learning algorithm with application to leader-follower robotic systems

Erfu Yang, Dongbing Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

7 Citations (Scopus)

Abstract

A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for dealing with the learning and control issues in cooperative multiagent systems with continuous states and actions, particularly for autonomous robotic formation systems. The parameters of fuzzy policy are finely tuned by the gradient multiagent reinforcement learning algorithm to improve the overall performance of an initial controller (policy). A leader-follower robotic system is chosen as a platform to benchmark the performance of the multiagent fuzzy policy reinforcement learning algorithm. Our simulation results demonstrate that the control performance can be improved in many aspects. This work also can be seen as a scaling up of currently popular multiagent reinforcement learning to the robotic domain with continuous state and action space as well as high dimensionality.

Original languageEnglish
Title of host publication2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages3197-3202
Number of pages6
ISBN (Print)1424402581
DOIs
Publication statusPublished - 2006
Event2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 - Beijing, United Kingdom
Duration: 9 Oct 200615 Oct 2006

Conference

Conference2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006
CountryUnited Kingdom
CityBeijing
Period9/10/0615/10/06

Keywords

  • cooperative control
  • fuzzy reinforcement learning
  • leader-follower robotic systems
  • policy gradient reinforcement learning
  • fuzzy control
  • gradient methods
  • learning (artificial intelligence)
  • multi-agent systems
  • multi-robot systems
  • position control

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  • Cite this

    Yang, E., & Gu, D. (2006). A multiagent fuzzy policy reinforcement learning algorithm with application to leader-follower robotic systems. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3197-3202). [4058888] IEEE. https://doi.org/10.1109/IROS.2006.282421