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A Convergence Framework for Deep $V$-Learning: Error Propagation and Sharp Action-Gap Bounds
We establish convergence bounds for deep $V$-learning with horizon $H$. The algorithm fits a scalar value function to targets from executed transitions and selects actions using a predictive model and the value function. For current observed-successor targets with fresh true-kernel outcomes, the con
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