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Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization
AG-OG is the first single-call algorithm with optimal convergence rates in both deterministic and stochastic settings for bilinearly coupled minimax optimization problems.
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL).
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