Order-Optimal Sample Complexity for Reinforcement Learning

Deep Reinforcement Learning (DRL) has seen tremendous advancements, yet achieving optimal sample complexity remains a fundamental challenge. This talk presents recent progress in developing order-optimal sample complexity guarantees for RL algorithms with general parametrization. We first discuss the key difficulties in achieving optimal sample complexity and introduce an accelerated natural policy gradient (ANPG) approach. We will further summarize some extensions of the approach. Our results bridge crucial gaps in RL theory, offering practical implications for scalable and efficient decision-making systems.