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arXiv · 2007.11120

On Linear Convergence of Policy Gradient Methods for Finite MDPs

Jalaj Bhandari·Daniel Russo
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Abstract

We revisit the finite time analysis of policy gradient methods in the one of the simplest settings: finite state and action MDPs with a policy class consisting of all stochastic policies and with exact gradient evaluations. There has been some recent work viewing this setting as an instance of smooth non-linear optimization problems and showing sub-linear convergence rates with small step-sizes. Here, we take a different perspective based on connections with policy iteration and show that many variants of policy gradient methods succeed with large step-sizes and attain a linear rate of convergence.

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Jalaj Bhandari, Daniel Russo. 2020-07-21. On Linear Convergence of Policy Gradient Methods for Finite MDPs. https://arxiv.org/abs/2007.11120

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