Accelerating Primal-dual Methods for Regularized Markov Decision Processes

Haoya Li; Hsiang-fu Yu; Lexing Ying; Inderjit Dhillon

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arXiv · 2202.10506

Accelerating Primal-dual Methods for Regularized Markov Decision Processes

Haoya Li·Hsiang-fu Yu·Lexing Ying·Inderjit Dhillon

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Abstract

Entropy regularized Markov decision processes have been widely used in reinforcement learning. This paper is concerned with the primal-dual formulation of the entropy regularized problems. Standard first-order methods suffer from slow convergence due to the lack of strict convexity and concavity. To address this issue, we first introduce a new quadratically convexified primal-dual formulation. The natural gradient ascent descent of the new formulation enjoys global convergence guarantee and exponential convergence rate. We also propose a new interpolating metric that further accelerates the convergence significantly. Numerical results are provided to demonstrate the performance of the proposed methods under multiple settings.

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BibTeX RIS

Haoya Li, Hsiang-fu Yu, Lexing Ying, Inderjit Dhillon. 2022-02-21. Accelerating Primal-dual Methods for Regularized Markov Decision Processes. https://arxiv.org/abs/2202.10506

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