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V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the action-value function into a state-dependent value component and action-specific advantages. While this mechanism has proven effective in both tabular an
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