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Reinforcement learning for planning of a simulated production line

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Author

  • Hugo Werner
  • Gustaf Ehn

Summary, in English

Deep reinforcement learning has been shown to be able to solve tasks without prior knowledge of thedynamics of the problems. In this thesis the applicability of reinforcement learning on the problem ofproduction planing is evaluated. Experiments are performed in order to reveal strengths and weak-nesses of the theory currently available. Reinforcement learning shows great potential but currentlyonly for a small class of problems. In order to use reinforcement learning to solve arbitrary or a largerclass of problems further work needs be done. This thesis was written at Syntronic Software Innova-tions.

Publishing year

2018

Language

English

Publication/Series

Master's Theses in Mathematical Sciences

Document type

Student publication for Master's degree (two years)

Topic

  • Technology and Engineering

Keywords

  • Reinforcement learning
  • Machine learning
  • artificial neural networks
  • production planning

Report number

LUTFMA-3341-2018

Supervisor

  • Niels Christian Overgaard (PhD (mathematics))
  • Adam Andersson

ISBN/ISSN/Other

  • ISSN: 1404-6342
  • 2018:E7