Reinforcement learning for planning of a simulated production line
(2018) In Master's Theses in Mathematical Sciences FMAM05 20172Mathematics (Faculty of Engineering)
- Abstract
- 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.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8936610
- author
- Werner, Hugo LU and Ehn, Gustaf LU
- supervisor
- organization
- course
- FMAM05 20172
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Reinforcement learning, Machine learning, artificial neural networks, production planning
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3341-2018
- ISSN
- 1404-6342
- other publication id
- 2018:E7
- language
- English
- id
- 8936610
- date added to LUP
- 2018-06-07 16:26:34
- date last changed
- 2024-09-24 12:32:29
@misc{8936610, abstract = {{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.}}, author = {{Werner, Hugo and Ehn, Gustaf}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Reinforcement learning for planning of a simulated production line}}, year = {{2018}}, }