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Reinforcement learning in schedulingThe goal of this research is to apply reinforcement learning methods to real-world problems like scheduling. In this preliminary paper, we show that learning to solve scheduling problems such as the Space Shuttle Payload Processing and the Automatic Guided Vehicle (AGV) scheduling can be usefully studied in the reinforcement learning framework. We discuss some of the special challenges posed by the scheduling domain to these methods and propose some possible solutions we plan to implement.
Document ID
Document Type
Conference Paper
Dietterich, Tom G.
(Oregon State Univ. Corvallis, OR, United States)
Ok, Dokyeong
(Oregon State Univ. Corvallis, OR, United States)
Zhang, Wei
(Oregon State Univ. Corvallis, OR, United States)
Tadepalli, Prasad
(Oregon State Univ. Corvallis, OR, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1994
Publication Information
Publication: NASA. Johnson Space Center, The Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), Volume 1
Subject Category
Behavioral Sciences
Accession Number
Funding Number(s)
Distribution Limits
Work of the US Gov. Public Use Permitted.
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