Learning Collusion in Episodic, Inventory-Constrained Markets

Paul Friedrich, Barna Pásztor, Giorgia Ramponi

Keywords: Collusion, Pricing, Multi-Agent Reinforcement Learning, Game Theory

TL;DR: We show how deep reinforcement learning agents can learn tacit collusion in complex markets with fixed supply and sell-by dates.

Abstract: Pricing algorithms have demonstrated the capability to learn tacit collusion that is largely unaddressed by current regulations. Their increasing use in markets, including oligopolistic industries with a history of collusion, calls for closer examination by competition authorities. In this paper, we extend the study of tacit collusion in learning algorithms from basic pricing games to more complex markets characterized by perishable goods with fixed supply and sell-by dates, such as airline tickets, perishables, or hotel rooms. We formalize collusion within this framework and introduce a metric based on price levels under both competitive (Nash) and collusive (monopolistic) equilibria. Since no analytical expressions for these price levels exist, we propose an efficient computational approach to derive them. Through experiments, we demonstrate that deep reinforcement learning agents can learn to collude in this more complex domain. Additionally, we analyze the underlying mechanisms and structures of the collusive strategies these agents adopt.

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Publication Link: https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p803.pdf

Submission Number: 56