On Linear Convergence of Policy Gradient Methods for Finite MDPs

• URL: http://arxiv.org/abs/2007.11120v2
• Date: Mon, 13 Dec 2021 17:32:23 GMT
• Title: On Linear Convergence of Policy Gradient Methods for Finite MDPs
• Authors: Jalaj Bhandari and Daniel Russo
• Abstract summary: We revisit the finite time analysis of policy gradient methods in the one of the simplest settings. We show that many variants of policy gradient methods succeed with large step-sizes and attain a linear rate of convergence.
• Score: 8.00114449574708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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