Q-Learning algorithms in a hotelling model
Date
2022-11
Authors
Porto, Lucila
relationships.isContributorOfPublication
Quesada, Lucía
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad de San Andrés. Departamento de Economía
Abstract
What if Q-Learning algorithms set not only prices but also the degree of differentiation between them? In this
paper, I tackle this question by analyzing the competition between two Q-Learning algorithms in a Hotelling
setting. I find that most of the simulations converge to a Nash Equilibrium where the algorithms are playing
non-competitive strategies. In most simulations, they optimally learn not to differentiate each other and to set a
supra-competitive price. An underlying deviation and punishment scheme sustains this implicit agreement. The
results are robust to the enlargement of the action space and the introduction of relocalization costs.
Keywords: Algorithmic Collusion, Reinforcement Learning, Q-Learning, Hotelling.
Keywords: Algorithmic Collusion, Reinforcement Learning, Q-Learning, Hotelling.
Description
Fil: Porto, Lucila. Universidad de San Andrés. Departamento de Economía; Argentina.