New theory bridges Newton-Raphson method and Regularized Policy Iteration

Researchers have established a formal equivalence between the Newton-Raphson method and Regularized Policy Iteration (RPI) when applied to regularized Markov Decision Processes (RMDPs). This connection, particularly evident when the regularizer is Shannon entropy, allows for a unified theoretical analysis of RPI and the development of accelerated algorithms. The study demonstrates that RPI exhibits local quadratic convergence, and proposes a new algorithm achieving third-order local convergence, supported by numerical experiments. AI

IMPACT Advances theoretical understanding of regularization in reinforcement learning, potentially leading to more efficient algorithm design.

RANK_REASON The cluster contains an academic paper detailing theoretical advancements in reinforcement learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

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• Bellman equation
• information entropy
• Regularized Markov Decision Processes
• Regularized Policy Iteration
• reinforcement learning
• Soft Actor--Critic
• Zeyang Li

• paper
• other

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