Several Reinforcement Learning Methods in Mean-Field Games with Binary Action Spaces

Recent years have witnessed significant progress in the sub-field of machine learning known as reinforcement learning, in which interactions between intelligent agents and the environment enable agents to learn and solve sequential decision-making problems through accumulating rewards with delays. Despite much success in single-player settings, reinforcement learning in multi-agent domains remains a challenging task in many aspects. In this thesis, the mean-field approach will be used to study binary action space stochastic games with a sufficiently large number of players that can be generalized to the multi-population case. Based on the mean-field approximation, several algorithms will be implemented and compared in numerical experiments to visualize their convergence to the equilibrium policy.

Name thesis:degree_name

Master of Science (M.Sc.)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Statistics

Grantor dc:publisher

Carleton University

Year dc:date.issued

2021

Author dc:creator

• Zhang, Chi

Statement dc:rights

• Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.

Language dc:language.iso

en

DOI

10.22215/etd/2021-14448

OAI identifier oai:identifier

oai:carleton.scholaris.ca:20.500.14718/42246