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Outstanding Paper Award Winners - 1/2 @ RLC 2025

TalkRL: The Reinforcement Learning Podcast

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Advancements in Reinforcement Learning Theory

This chapter examines a groundbreaking paper that introduces a generalized projected Bellman error in reinforcement learning. It contrasts traditional semi-gradient TD methods with gradient TD methods, showcasing their superior convergence and practical advantages in deep reinforcement learning applications.

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