Reinforcement learning approach to support setup decisions in distributed manufacturing systems

Patrick McDonnell, Sanjay Joshi

Research output: Contribution to conferencePaper

2 Scopus citations

Abstract

A reinforcement learning approach to specifying payoffs for setup games is presented. Setup games are normal form, non-cooperative games used by heterarchical machine controllers to evaluate reconfiguration decisions. While past work utilizing heuristic measures to approximate the effect of setup decisions has demonstrated promising performance, the lack of an accurate long-term model of system dynamics in these heuristic approaches limits their usefulness. The reinforcement learning approach iteratively learns the long term costs of setup decisions, accounting for both immediate decision effects and the effects of likely downstream decisions.

Original languageEnglish (US)
Pages221-225
Number of pages5
Publication statusPublished - Jan 1 1997
EventProceedings of the 1997 IEEE 6th International Conference on Emerging Technologies and Factory Automation, ETFA'97 - Los Angeles, CA, USA
Duration: Sep 9 1997Sep 12 1997

Other

OtherProceedings of the 1997 IEEE 6th International Conference on Emerging Technologies and Factory Automation, ETFA'97
CityLos Angeles, CA, USA
Period9/9/979/12/97

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

McDonnell, P., & Joshi, S. (1997). Reinforcement learning approach to support setup decisions in distributed manufacturing systems. 221-225. Paper presented at Proceedings of the 1997 IEEE 6th International Conference on Emerging Technologies and Factory Automation, ETFA'97, Los Angeles, CA, USA, .