This paper studies the aspiration-based learning dynamics in symmetric normal-form games played at multiple locations. In particular, the aspiration level in one location is linked to the average performances of players in observable locations. With a decentralized information structure, the learning dynamics converge to limit states. For a large class of information structures and games, when there exists trembles in the updating of aspiration levels, the unique stochastically stable equilibrium is characterized by the Pareto efficient symmetric outcome. In the prisoners' dilemma, if the probability of trembles is sufficiently small, both players in every location will ultimately cooperate most of the time.