Cover Image

Tractable Representations for Convergent Approximation of Distributional HJB Equations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Alhosh, Julie, Wiltzer, Harley, Meger, David
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
by: Wiltzer, Harley, et al.
Published: (2024)

Foundations of Multivariate Distributional Reinforcement Learning
by: Wiltzer, Harley, et al.
Published: (2024)

Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
by: Jhaveri, Yash, et al.
Published: (2025)

Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problems
by: Yang, Xianjin, et al.
Published: (2025)

Convergence of Policy Mirror Descent Beyond Compatible Function Approximation
by: Sherman, Uri, et al.
Published: (2025)

Diagonalizing the Softmax: Hadamard Initialization for Tractable Cross-Entropy Dynamics
by: Garrod, Connall, et al.
Published: (2025)

Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
by: Boone, Victor, et al.
Published: (2024)

Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach
by: Zhang, Fenglin, et al.
Published: (2026)

Convergence of Distributed Adaptive Optimization with Local Updates
by: Cheng, Ziheng, et al.
Published: (2024)

Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
by: Cayci, Semih, et al.
Published: (2021)

Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings
by: Jiang, Wei, et al.
Published: (2025)

Convergence Rate in Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence
by: Chen, Zixi, et al.
Published: (2025)

Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise
by: Qian, Xiaochi, et al.
Published: (2024)

Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation
by: Zhang, Yixuan, et al.
Published: (2024)

Non-Parametric Learning of Stochastic Differential Equations with Non-asymptotic Fast Rates of Convergence
by: Bonalli, Riccardo, et al.
Published: (2023)

Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift
by: Liu, Xinyu, et al.
Published: (2026)

Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025)

An Approximate Ascent Approach To Prove Convergence of PPO
by: Doering, Leif, et al.
Published: (2026)

Central Limit Theorem for Two-Time-Scale Approximate Distributionally Robust RL
by: Wang, Shengbo, et al.
Published: (2026)

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2023)

Tractable hierarchies of convex relaxations for polynomial optimization on the nonnegative orthant
by: Mai, Ngoc Hoang Anh, et al.
Published: (2022)

From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
by: Xie, Shengping, et al.
Published: (2025)

Distributed Random Reshuffling Methods with Improved Convergence
by: Huang, Kun, et al.
Published: (2023)

Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
by: Robin, David A. R., et al.
Published: (2025)

Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence
by: Jiao, Yang, et al.
Published: (2024)

Linear Convergence of the Frank-Wolfe Algorithm over Product Polytopes
by: Iommazzo, Gabriele, et al.
Published: (2025)

Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
by: Kaya, Ege C., et al.
Published: (2026)

A Retrospective Approximation Approach for Smooth Stochastic Optimization
by: Newton, David, et al.
Published: (2021)

Singular Perturbations of Nonlocal HJB Equations in Multiscale Stochastic Control
by: Zhang, Qi, et al.
Published: (2024)

GANs as Gradient Flows that Converge
by: Huang, Yu-Jui, et al.
Published: (2022)

Convergence Rate Analysis of LION
by: Dong, Yiming, et al.
Published: (2024)

Convergence of Muon with Newton-Schulz
by: Kim, Gyu Yeol, et al.
Published: (2026)

Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
by: Lin, Yifan, et al.
Published: (2024)

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
by: Levin, Ilya, et al.
Published: (2026)

Follow The Approximate Sparse Leader for No-Regret Online Sparse Linear Approximation
by: Mukhopadhyay, Samrat, et al.
Published: (2025)

The Convergence of Dynamic Routing between Capsules
by: Ye, Daoyuan, et al.
Published: (2025)

Convergence for Discrete Parameter Update Schemes
by: Wilson, Paul, et al.
Published: (2025)

Provably Convergent Federated Trilevel Learning
by: Jiao, Yang, et al.
Published: (2023)

Viscosity Solutions for HJB Equations on the Process Space
by: Zhou, Jianjun, et al.
Published: (2024)