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

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Creator: 

Zhang, Chi

Date: 

2021

Abstract: 

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.

Subject: 

Statistics
Artificial Intelligence

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Science: 
M.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Statistics

Parent Collection: 

Theses and Dissertations

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