The use of pricing algorithms raises concerns about algorithmic collusion. This paper considers a sequential pricing model where marginal cost fluctuates over time. I find that Q-learning algorithms autonomously collude even under cost uncertainty. Collusion is sustained by strategies that involve reward-punishment schemes. It suggests that cost uncertainty is not an obstacle to autonomous algorithmic collusion.