Image de couverture de livre

Weak Convergence Analysis of Online Neural Actor-Critic Algorithms

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Lam, Samuel Chun-Hei, Sirignano, Justin, Wang, Ziheng
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Optimization and Control Probability
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces
par: Fouque, Jean-Pierre, et autres
Publié: (2025)

Convergence Analysis of Real-time Recurrent Learning (RTRL) for a class of Recurrent Neural Networks
par: Lam, Samuel Chun-Hei, et autres
Publié: (2025)

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
par: Tanguy, Eloi
Publié: (2023)

Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes
par: Zeng, Sihan, et autres
Publié: (2021)

Scaling Effects and Uncertainty Quantification in Neural Actor Critic Algorithms
par: Georgoudios, Nikos, et autres
Publié: (2026)

Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
par: Sato, Kanji, et autres
Publié: (2022)

Convergence rates for the Adam optimizer
par: Dereich, Steffen, et autres
Publié: (2024)

A Generalization Result for Convergence in Learning-to-Optimize
par: Sucker, Michael, et autres
Publié: (2024)

Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
par: Kassing, Sebastian, et autres
Publié: (2025)

Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA
par: Zhang, Yixuan, et autres
Publié: (2024)

Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
par: Bruno, Stefano, et autres
Publié: (2025)

Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution
par: Chen, Xinyun, et autres
Publié: (2023)

Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
par: Srikant, R.
Publié: (2024)

Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies
par: Cohen, Samuel N., et autres
Publié: (2025)

Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
par: Srinivasan, Vishwak, et autres
Publié: (2025)

Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization
par: Cory-Wright, Ryan, et autres
Publié: (2025)

Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise
par: Dang, Thanh, et autres
Publié: (2025)

Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
par: Lim, Dong-Young, et autres
Publié: (2021)

Kernel Limit for a Class of Recurrent Neural Networks Trained on Ergodic Data Sequences
par: Lam, Samuel Chun-Hei, et autres
Publié: (2023)

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
par: Altekrüger, Fabian, et autres
Publié: (2023)

A Communication-Efficient Decentralized Actor-Critic Algorithm
par: Ren, Xiaoxing, et autres
Publié: (2025)

Online Resource Allocation with Average Budget Constraints
par: Ao, Ruicheng, et autres
Publié: (2024)

The Performance Of The Unadjusted Langevin Algorithm Without Smoothness Assumptions
par: Johnston, Tim, et autres
Publié: (2025)

Convergence rate of Tsallis entropic regularized optimal transport
par: Suguro, Takeshi, et autres
Publié: (2023)

Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
par: Zeng, Sihan, et autres
Publié: (2024)

Convergence of linear programming hierarchies for Gibbs states of spin systems
par: Fawzi, Hamza, et autres
Publié: (2025)

Convergence Analysis of the PAGE Stochastic Algorithm for Weakly Convex Finite-Sum Optimization
par: Condat, Laurent, et autres
Publié: (2025)

Convergence Analysis of Newton's Method for Neural Networks in the Overparameterized Limit
par: Riedl, Konstantin, et autres
Publié: (2026)

Convergence of coordinate ascent variational inference for log-concave measures via optimal transport
par: Arnese, Manuel, et autres
Publié: (2024)

Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow
par: Li, Qin, et autres
Publié: (2024)

Convergence of Distributed Adaptive Optimization with Local Updates
par: Cheng, Ziheng, et autres
Publié: (2024)

Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space
par: Chen, Zhengdao
Publié: (2023)

Neural Brownian Motion
par: Qi, Qian
Publié: (2025)

Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
par: Agrawalla, Bhavya, et autres
Publié: (2023)

Beyond Non-Degeneracy: Revisiting Certainty Equivalent Heuristic for Online Linear Programming
par: Chen, Yilun, et autres
Publié: (2025)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
par: Han, Yuze, et autres
Publié: (2024)

Asymptotic regularity of a generalised stochastic Halpern scheme
par: Pischke, Nicholas, et autres
Publié: (2024)

Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
par: Hu, Jie, et autres
Publié: (2024)

Learning-Based Pricing and Matching for Two-Sided Queues
par: Yang, Zixian, et autres
Publié: (2024)

Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance
par: Gao, Rui, et autres
Publié: (2024)