Distributed Reinforcement Learning Based Optimal Controller for Mobile Robot Formation

Chinmay Shinde, Kaushik Das, Swagat Kumar, Laxmidhar Behera

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper addresses a problem of attaining desired geometric formation for a group of homogeneous robots using distributed reinforcement learning. The challenges for learning by experience requires huge time and data samples. In multi-agent system (MAS), individual learning becomes more complex as it has to cooperate with its neighboring agent. In this work, a group of homogeneous robots models a single controller while performing a task in a decentralized manner. The framework uses an actor-critic architecture for local learning and its update law is identified using Lyapunov stability analysis. However, a global single controller is achieved by using average consensus protocol. Simulation as well as the experimental results have been given to demonstrate the proposed algorithm.
Original languageEnglish
Title of host publication2018 European Control Conference, ECC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2800-2805
Number of pages6
ISBN (Print)9783952426982
DOIs
Publication statusPublished - 27 Nov 2018
Event2018 European Control Conference, ECC 2018 - Limassol, Cyprus
Duration: 12 Jun 201815 Jun 2018

Publication series

Name2018 European Control Conference, ECC 2018

Conference

Conference2018 European Control Conference, ECC 2018
CountryCyprus
CityLimassol
Period12/06/1815/06/18

Keywords

  • Multi-agent systems
  • actor-critic network
  • distributed reinforcement learning
  • formation control.

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  • Cite this

    Shinde, C., Das, K., Kumar, S., & Behera, L. (2018). Distributed Reinforcement Learning Based Optimal Controller for Mobile Robot Formation. In 2018 European Control Conference, ECC 2018 (pp. 2800-2805). (2018 European Control Conference, ECC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ECC.2018.8550590