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We study a dynamic setting in which a public information platform updates a belief estimate of a continuous game parameter based on available data of strategies and payoffs. Players adjust their strategies by accounting for the repeatedly updated belief. The long-term behavior of the resulting stochastic learning dynamics is based on endogenous and non-i.i.d. data that is generated by players’ strategic decisions. We develop new tools to tackle the dynamic interplay between parameter learning and strategy learning in continuous games. We present results on the convergence and stability of such learning dynamics and develop conditions for convergence to complete information equilibrium. Furthermore, we apply this learning model to analyze the impact of information platforms on the strategic behavior of travelers in urban transportation systems. We show that our results can be used to design adaptive tolling mechanisms with travelers learning their routing decisions in traffic networks.