Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
Fuente:
arXiv
Guardado en:
| Autores principales: | Sherman, Uri, Koren, Tomer, Mansour, Yishay |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
por: Sherman, Uri, et al.
Publicado: (2025)
por: Sherman, Uri, et al.
Publicado: (2025)
The Hidden Cost of Approximation in Online Mirror Descent
por: Schlisselberg, Ofir, et al.
Publicado: (2025)
por: Schlisselberg, Ofir, et al.
Publicado: (2025)
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
por: Attia, Amit, et al.
Publicado: (2025)
por: Attia, Amit, et al.
Publicado: (2025)
On the Convergence of Policy in Unregularized Policy Mirror Descent
por: Lin, Dachao, et al.
Publicado: (2022)
por: Lin, Dachao, et al.
Publicado: (2022)
Rate-Optimal Policy Optimization for Linear Markov Decision Processes
por: Sherman, Uri, et al.
Publicado: (2023)
por: Sherman, Uri, et al.
Publicado: (2023)
On the Convergence of Policy Mirror Descent with Temporal Difference Evaluation
por: Liu, Jiacai, et al.
Publicado: (2025)
por: Liu, Jiacai, et al.
Publicado: (2025)
Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization
por: Vansover-Hager, Shira, et al.
Publicado: (2025)
por: Vansover-Hager, Shira, et al.
Publicado: (2025)
Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
por: Akhtiamov, Danil, et al.
Publicado: (2026)
por: Akhtiamov, Danil, et al.
Publicado: (2026)
Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
por: Attia, Amit, et al.
Publicado: (2025)
por: Attia, Amit, et al.
Publicado: (2025)
How Free is Parameter-Free Stochastic Optimization?
por: Attia, Amit, et al.
Publicado: (2024)
por: Attia, Amit, et al.
Publicado: (2024)
Regret Minimization and Convergence to Equilibria in General-sum Markov Games
por: Erez, Liad, et al.
Publicado: (2022)
por: Erez, Liad, et al.
Publicado: (2022)
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
por: Alfano, Carlo, et al.
Publicado: (2023)
por: Alfano, Carlo, et al.
Publicado: (2023)
Faster Stochastic Optimization with Arbitrary Delays via Asynchronous Mini-Batching
por: Attia, Amit, et al.
Publicado: (2024)
por: Attia, Amit, et al.
Publicado: (2024)
Parameter-free Mirror Descent
por: Jacobsen, Andrew, et al.
Publicado: (2022)
por: Jacobsen, Andrew, et al.
Publicado: (2022)
Mirror Descent on Riemannian Manifolds
por: Jiang, Jiaxin, et al.
Publicado: (2026)
por: Jiang, Jiaxin, et al.
Publicado: (2026)
A Mirror Descent Perspective of Smoothed Sign Descent
por: Wang, Shuyang, et al.
Publicado: (2024)
por: Wang, Shuyang, et al.
Publicado: (2024)
Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints
por: Alkousa, Mohammad S., et al.
Publicado: (2026)
por: Alkousa, Mohammad S., et al.
Publicado: (2026)
Second-Order Mirror Descent: Convergence in Games Beyond Averaging and Discounting
por: Gao, Bolin, et al.
Publicado: (2021)
por: Gao, Bolin, et al.
Publicado: (2021)
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
por: Cayci, Semih, et al.
Publicado: (2021)
por: Cayci, Semih, et al.
Publicado: (2021)
Policy Mirror Descent with Temporal Difference Learning: Sample Complexity under Online Markov Data
por: Li, Wenye, et al.
Publicado: (2025)
por: Li, Wenye, et al.
Publicado: (2025)
Mirror Descent on Reproducing Kernel Banach Spaces
por: Kumar, Akash, et al.
Publicado: (2024)
por: Kumar, Akash, et al.
Publicado: (2024)
Mirror and Preconditioned Gradient Descent in Wasserstein Space
por: Bonet, Clément, et al.
Publicado: (2024)
por: Bonet, Clément, et al.
Publicado: (2024)
Value Mirror Descent for Reinforcement Learning
por: Jia, Zhichao, et al.
Publicado: (2026)
por: Jia, Zhichao, et al.
Publicado: (2026)
Learning Provably Improves the Convergence of Gradient Descent
por: Song, Qingyu, et al.
Publicado: (2025)
por: Song, Qingyu, et al.
Publicado: (2025)
Convergence of Spectral Descent for Non-smooth Optimization
por: Yang, Yixuan, et al.
Publicado: (2026)
por: Yang, Yixuan, et al.
Publicado: (2026)
Convergence of Alternating Gradient Descent for Matrix Factorization
por: Ward, Rachel, et al.
Publicado: (2023)
por: Ward, Rachel, et al.
Publicado: (2023)
Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization
por: Gu, Zhihao, et al.
Publicado: (2024)
por: Gu, Zhihao, et al.
Publicado: (2024)
Mirror Descent-Ascent for mean-field min-max problems
por: Lascu, Razvan-Andrei, et al.
Publicado: (2024)
por: Lascu, Razvan-Andrei, et al.
Publicado: (2024)
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
por: Klein, Sara, et al.
Publicado: (2023)
por: Klein, Sara, et al.
Publicado: (2023)
On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
por: Qin, Zhen, et al.
Publicado: (2025)
por: Qin, Zhen, et al.
Publicado: (2025)
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
por: Li, Tianyou, et al.
Publicado: (2023)
por: Li, Tianyou, et al.
Publicado: (2023)
Open Problem: Anytime Convergence Rate of Gradient Descent
por: Kornowski, Guy, et al.
Publicado: (2024)
por: Kornowski, Guy, et al.
Publicado: (2024)
Random Function Descent
por: Benning, Felix, et al.
Publicado: (2023)
por: Benning, Felix, et al.
Publicado: (2023)
Towards Fully Parameter-Free Stochastic Optimization: Grid Search with Self-Bounding Analysis
por: Zhao, Yuheng, et al.
Publicado: (2026)
por: Zhao, Yuheng, et al.
Publicado: (2026)
Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence
por: Datar, Adwait, et al.
Publicado: (2025)
por: Datar, Adwait, et al.
Publicado: (2025)
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification
por: Jung, Hyunji, et al.
Publicado: (2025)
por: Jung, Hyunji, et al.
Publicado: (2025)
Exponential Convergence of (Stochastic) Gradient Descent for Separable Logistic Regression
por: Kale, Sacchit, et al.
Publicado: (2026)
por: Kale, Sacchit, et al.
Publicado: (2026)
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation
por: Mishkin, Aaron, et al.
Publicado: (2024)
por: Mishkin, Aaron, et al.
Publicado: (2024)
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
por: Ma, Jianhao, et al.
Publicado: (2024)
por: Ma, Jianhao, et al.
Publicado: (2024)
On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
por: Kong, Boao, et al.
Publicado: (2026)
por: Kong, Boao, et al.
Publicado: (2026)
Ejemplares similares
-
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
por: Sherman, Uri, et al.
Publicado: (2025) -
The Hidden Cost of Approximation in Online Mirror Descent
por: Schlisselberg, Ofir, et al.
Publicado: (2025) -
Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
por: Attia, Amit, et al.
Publicado: (2025) -
On the Convergence of Policy in Unregularized Policy Mirror Descent
por: Lin, Dachao, et al.
Publicado: (2022) -
Rate-Optimal Policy Optimization for Linear Markov Decision Processes
por: Sherman, Uri, et al.
Publicado: (2023)