Flow-shop robotic scheduling with collaborative reinforcement learning

Helman Stern, Kfir Arviv, Yael Edan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A collaborative reinforcement learning (RL) method for minimizing make-span in a robotic flow-shop scheduling problem is presented. The robot can operate either autonomously (no adviser) or semi-autonomously (with adviser). In autonomous mode, the robot uses RL ε-greedy selection scheme. In semi-autonomous mode a collaborative agent (adviser) provides advice to the robot. The robot is endowed with three cognitive abilities: (i) ability to assess its own performance, using an adaptive performance threshold to switch between collaborative modes, (ii) short term ability to assess good and bad advice, and to accept or reject it, (iii) and long term ability to assess advisor's skill levels, and discontinue collaborating with novice advisors. Adviser's behaviors are simulated by various skill levels, represented by softmax action selection distributions. The collaborative robot-adviser system average error was, at the most, 9.4% within a lower-bound value. An expert adviser was found to accelerate the robot learning process.

Original languageEnglish
Title of host publication21st International Conference on Production Research
Subtitle of host publicationInnovation in Product and Production, ICPR 2011 - Conference Proceedings
EditorsTobias Krause, Dieter Spath, Rolf Ilg
PublisherFraunhofer-Verlag
ISBN (Electronic)9783839602935
StatePublished - 1 Jan 2011
Event21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Stuttgart, Germany
Duration: 31 Jul 20114 Aug 2011

Publication series

Name21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings

Conference

Conference21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011
Country/TerritoryGermany
CityStuttgart
Period31/07/114/08/11

Keywords

  • Collaboration
  • Flow-Shop
  • Job transfer robots
  • Reinforcement learning
  • Scheduling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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