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This paper presents a two-stage learning pipeline that uses an attention-augmented gated recurrent network to estimate wind from onboard kinematics and then leverages that estimate in a reinforcement learning controller to improve quadrotor trajectory tracking under turbulent winds, reducing tracking error by 48% compared to a baseline.