Imagen de Portada

Deep Backward and Galerkin Methods for the Finite State Master Equation

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Cohen, Asaf, Laurière, Mathieu, Zell, Ethan
Formato:	Preprint
Publicado:	2024
Materias:	Optimization and Control Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Asymptotic Nash Equilibria of Finite-State Ergodic Markovian Mean Field Games
por: Cohen, Asaf, et al.
Publicado: (2024)

Operator Learning for Families of Finite-State Mean-Field Games
por: Hofgard, William, et al.
Publicado: (2026)

Deep Learning for the Multiple Optimal Stopping Problem
por: Laurière, Mathieu, et al.
Publicado: (2025)

Recent Developments in Machine Learning Methods for Stochastic Control and Games
por: Hu, Ruimeng, et al.
Publicado: (2023)

Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models
por: Gu, Zhouzhou, et al.
Publicado: (2024)

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
por: Magnino, Lorenzo, et al.
Publicado: (2024)

Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods
por: Carmona, René, et al.
Publicado: (2019)

Population-Aware Imitation Learning in Mean-field Games with Common Noise
por: Lambrecht, Grégoire, et al.
Publicado: (2026)

An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
por: Hua, Mengjian, et al.
Publicado: (2024)

A Deep Learning Method for Optimal Investment Under Relative Performance Criteria Among Heterogeneous Agents
por: Laurière, Mathieu, et al.
Publicado: (2024)

Reinforcement Learning for Finite Space Mean-Field Type Games
por: Shao, Kai, et al.
Publicado: (2024)

Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces
por: Fouque, Jean-Pierre, et al.
Publicado: (2025)

Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games
por: Angiuli, Andrea, et al.
Publicado: (2024)

Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
por: Magnino, Lorenzo, et al.
Publicado: (2025)

Machine Learning Methods for Large Population Games with Applications in Operations Research
por: Dayanikli, Gokce, et al.
Publicado: (2024)

Convergence of the Deep Galerkin Method for Mean Field Control Problems
por: Hofgard, William, et al.
Publicado: (2024)

Integration Matters for Learning PDEs with Backward SDEs
por: Park, Sungje, et al.
Publicado: (2025)

Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
por: Magnino, Lorenzo, et al.
Publicado: (2026)

Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
por: Kotary, James, et al.
Publicado: (2023)

On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
por: Kong, Boao, et al.
Publicado: (2026)

Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
por: Blaser, Ethan, et al.
Publicado: (2024)

How can the tragedy of the commons be prevented?: Introducing Linear Quadratic Mixed Mean Field Games
por: Dayanikli, Gokce, et al.
Publicado: (2024)

Cooperation, Competition, and Common Pool Resources in Mean Field Games
por: Dayanikli, Gokce, et al.
Publicado: (2025)

Discrete-Time Mean Field Type Games: Probabilistic Setup
por: Lambrecht, Grégoire, et al.
Publicado: (2025)

An Overview of Some Extensions of Mean Field Games beyond Perfect Homogeneity and Anonymity
por: Laurière, Mathieu
Publicado: (2025)

Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions
por: Mehmood, Sheheryar, et al.
Publicado: (2022)

A Double Inertial Forward-Backward Splitting Algorithm With Applications to Regression and Classification Problems
por: Işik, İrfan, et al.
Publicado: (2025)

Landscape of Policy Optimization for Finite Horizon MDPs with General State and Action
por: Chen, Xin, et al.
Publicado: (2024)

Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
por: Scaman, Kevin, et al.
Publicado: (2023)

Deep Signature Approach for McKean-Vlasov FBSDEs in a Random Environment
por: Hu, Ruimeng, et al.
Publicado: (2025)

Bundle Network: a Machine Learning-Based Bundle Method
por: Demelas, Francesca, et al.
Publicado: (2025)

Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning
por: van Twiller, Jaike, et al.
Publicado: (2025)

Learning Constrained Optimization with Deep Augmented Lagrangian Methods
por: Kotary, James, et al.
Publicado: (2024)

Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
por: Tran, Hoang, et al.
Publicado: (2024)

VFOG: Variance-Reduced Fast Optimistic Gradient Methods for a Class of Nonmonotone Generalized Equations
por: Tran-Dinh, Quoc, et al.
Publicado: (2025)

Lyapunov Analysis For Monotonically Forward-Backward Accelerated Algorithms
por: Fu, Mingwei, et al.
Publicado: (2024)

Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables
por: Brevault, Loic, et al.
Publicado: (2023)

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
por: Peitz, Sebastian, et al.
Publicado: (2024)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
por: Ding, Kuangyu, et al.
Publicado: (2023)

Dual Lagrangian Learning for Conic Optimization
por: Tanneau, Mathieu, et al.
Publicado: (2024)