Max weight learning algorithms for scheduling in unknown environments

Michael J. Neely, Scott T. Rager, Thomas F. La Porta

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


We consider a discrete time queueing system where a controller makes a 2-stage decision every slot. The decision at the first stage reveals a hidden source of randomness with a control-dependent (but unknown) probability distribution. The decision at the second stage generates an attribute vector that depends on this revealed randomness. The goal is to stabilize all queues and optimize a utility function of time average attributes, subject to an additional set of time average constraints. This setting fits a wide class of stochastic optimization problems, including multi-user wireless scheduling with dynamic channel measurement decisions, and wireless multi-hop routing with multi-receiver diversity and opportunistic routing decisions. We develop a simple max-weight algorithm that learns efficient behavior by averaging functionals of previous outcomes.

Original languageEnglish (US)
Article number6174451
Pages (from-to)1179-1191
Number of pages13
JournalIEEE Transactions on Automatic Control
Issue number5
StatePublished - May 2012

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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