Deep Q-Learning on Hölder Spaces
Researchers have published a paper on arXiv detailing a theoretical advancement in Q-learning, a fundamental algorithm in reinforcement learning. The study focuses on the mathematical underpinnings of Q-learning within continuous state and action spaces, specifically analyzing the Bellman optimality target. The paper proposes a DeepONet architecture tailored to the mixed regularity properties of the problem and derives approximation bounds, highlighting a trade-off between stiffness and complexity as the time step approaches zero. AI
IMPACT Advances theoretical understanding of reinforcement learning algorithms, potentially informing future practical applications.