Equilibrium stability as a driver of cooperation among Q-learners

Jul 16, 2026

A new preprint investigates how Q-learning agents with constant exploration can exhibit cooperative behavior in repeated games. The authors derive a theoretical boundary that predicts when cooperation will dominate in the time-averaged behavior of these agents, and validate their predictions with extensive simulations. Their analysis moves beyond traditional convergence assumptions by focusing on persistent exploration, which is more realistic for deployed algorithms.

Why it matters: This work advances understanding of when AI-driven pricing algorithms might sustain cooperative, potentially anti-competitive outcomes, informing debates on algorithmic collusion and regulatory policy.

Full story at: arXiv Multiagent Systems ↗