On the Convergence of Policy in Unregularized Policy Mirror Descent
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Abstract (arXiv)
In this short note, we give the convergence analysis of the policy in the recent famous policy mirror descent (PMD). We mainly consider the unregularized setting following [11] with generalized Bregman divergence. The difference is that we directly give the convergence rates of policy under generalized Bregman divergence. Our results are inspired by the convergence of value function in previous works and are an extension study of policy mirror descent. Though some results have already appeared in previous work, we further discover a large body of Bregman divergences could give finite-step convergence to an optimal policy, such as the classical Euclidean distance.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 17 May 2022.
Builds on
7 of 13 references.
References · 7
- 2020Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularizationcited by 170
- 2021Policy Mirror Descent for Reinforcement Learning: Linear Convergence, New Sampling Complexity, and Generalized Problem Classescited by 149
- 2022On the Convergence Rates of Policy Gradient Methodscited by 115
- 2021Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergencecited by 83
- 2021On the Linear convergence of Natural Policy Gradient Algorithmcited by 48
- 2022Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexitycited by 14
- 2022Block Policy Mirror Descentcited by 10
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Citing papers per year: 2026: 1.
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