Determining policy for a system dynamics model using reinforcement learning
Name
1263351016-MIT.pdf
Size
1.36 MB
Format
Adobe PDF
Checksum (MD5)
5f50183f0f3f6d54d27512fddb8f13b9
Author(s)
Thomas, Aditya.
Advisor(s)
Hazhir Rahmandad.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
System dynamics allows managers and policy makers to analyze problems with non-linear feedback structures and thus counter-intuitive behavior. A main tool of system dynamics is to build a computational model of a system and analyze it to determine suitable policies to move the system to a desired goal. This work aims at using methods and algorithms from reinforcement learning to determine suitable policies for a system dynamics model. We introduce the techniques, methods and algorithms of reinforcement learning and apply them to a classical model from the system dynamics literature.
Description
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, September, 2020
Cataloged from the official version of thesis.
Includes bibliographical references (pages 42-43).
Subjects
Engineering and Management Program.
System Design and Management Program.
MIT Department
Massachusetts Institute of Technology. Engineering and Management Program
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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