Primal-Dual Stochastic Mirror Descent for MDPs
Not on the map. Why?
Abstract (arXiv)
We consider the problem of learning the optimal policy for infinite-horizon Markov decision processes (MDPs). For this purpose, some variant of Stochastic Mirror Descent is proposed for convex programming problems with Lipschitz-continuous functionals. An important detail is the ability to use inexact values of functional constraints and compute the value of dual variables. We analyze this algorithm in a general case and obtain an estimate of the convergence rate that does not accumulate errors during the operation of the method. Using this algorithm, we get the first parallel algorithm for mixing average-reward MDPs with a generative model without reduction to discounted MDP. One of the main features of the presented method is low communication costs in a distributed centralized setting, even with very large networks.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 27 Feb 2021.
Builds on
9 of 31 references.
References · 9
- 2019Model-Based Reinforcement Learning with a Generative Model is Minimax Optimalcited by 124
- 2020Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Modelcited by 108
- 2017Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processescited by 56
- 2020Efficiently Solving MDPs with Stochastic Mirror Descentcited by 52
- 2017Primal-Dual π Learning: Sample Complexity and Sublinear Run Time for Ergodic Markov Decision Problemscited by 32
- 2013On Lower Complexity Bounds for Large-Scale Smooth Convex Optimizationcited by 24
- 2017Mirror Descent and Convex Optimization Problems With Non-Smooth Inequality Constraintscited by 10
- 2016Algorithms for stochastic optimization with functional or expectation constraintscited by 2
- 2018On Some Adaptive Mirror Descent Algorithms for Convex and Strongly Convex Optimization Problems with Functional Constraintscited by 1
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.