Author ORCID Identifier
Date Available
4-30-2023
Year of Publication
2023
Degree Name
Doctor of Philosophy (PhD)
Document Type
Doctoral Dissertation
College
Engineering
Department/School/Program
Computer Science
First Advisor
Dr. Judy Goldsmith
Second Advisor
Dr. Brent Harrison
Abstract
Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. We use game-theoretic evaluation criteria such as optimality, stability, and resulting equilibria.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2023.117
Recommended Citation
Taywade, Kshitija, "Multi-agent Learning For Game-theoretical Problems" (2023). Theses and Dissertations--Computer Science. 128.
https://uknowledge.uky.edu/cs_etds/128
