Using reinforcement learning to improve network durability

Authors
Hammel, Erik
ORCID
Loading...
Thumbnail Image
Other Contributors
Mitchell, John E.
Bennett, Kristin P.
Ecker, Joseph G.
Sharkey, Thomas C.
Wallace, William A., 1935-
Issue Date
2013-05
Keywords
Mathematics
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Our goal is to determine and optimize the efficacy of reinforcing an existing flow network to prevent unmet demand from imminent disruptions. We are given probabilities of failures for edges in the network and are asked to find edges which will best provide durability to the network post-event. The problem is extended to multiple time steps to address concerns of available resources versus quality of installations: the farther away from the event one makes decisions the more resources are available but the less reliable the uncertainty information. This sequential decision-making process is a classic example of dynamic programming. To avoid the "curses of dimensionality", we formulate an approximate dynamic program. To improve performance, especially as applied to flow networks, we derive several innovative adaptations from reinforcement learning concepts. This involves developing a policy, a function that makes installation decisions when given current forecast information, in a two step process: policy evaluation and policy improvement.
Description
May 2013
School of Science
Department
Dept. of Mathematical Sciences
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.