Determining policy for a system dynamics model using reinforcement learning

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Engineering and Management Program

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Thomas, Aditya.

Advisor dc:contributor.advisor

• Hazhir Rahmandad.

• Engineering and Management Program.
• System Design and Management Program.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/132832

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/132832