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We introduce Coarse Q-learning, a reinforcement learning framework in which a decision-maker partitions alternatives into coarse similarity classes, chooses among available classes using logit, and learns class valuations by averaging experienced payoffs. Even in stationary bandit problems with independently drawn menus, choices change the composition of pooled feedback. By deriving the continuous-time mean field ODE limit, we show this endogenous sampling can generate a unique globally attracting mixed equilibrium with limiting indifference, multiple stable strict rankings, or persistent cycling. Learning can reduce welfare even when the coarse representation admits efficient choice. An application demonstrates how digital platforms may exploit this feedback by promoting high-quality products to sustain purchases of inferior products under a common brand.