Cover Image

Convergence of Neural Network Policies for Risk--Reward Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Chang, Dang, Duy-Minh
Format:	Preprint
Published:	2026
Subjects:	Computational Finance 93E20, 68T07, 90C15, 62G05
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Money-Back Tontines for Retirement Decumulation: Neural-Network Optimization under Systematic Longevity Risk
by: Orozco, German Nova, et al.
Published: (2026)

Neural Network Convergence for Variational Inequalities
by: Zhao, Yun, et al.
Published: (2025)

Sample Complexity of Policy Gradient for Log-Growth Control
by: Pan, Qiuhua, et al.
Published: (2026)

A data-driven Fourier-mixture neural-network method for density estimation
by: Dang, Duy-Minh, et al.
Published: (2026)

Reinforcement Learning Methods for the Stochastic Optimal Control of an Industrial Power-to-Heat System
by: Pilling, Eric, et al.
Published: (2024)

An optimal experimental design approach to sensor placement in continuous stochastic filtering
by: Pathiraja, Sahani, et al.
Published: (2025)

KFCPO: Kronecker-Factored Approximated Constrained Policy Optimization
by: Lim, Joonyoung, et al.
Published: (2025)

Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty
by: Jia, Yanwei
Published: (2024)

A monotone piecewise constant control integration approach for the two-factor uncertain volatility model
by: Dang, Duy-Minh, et al.
Published: (2024)

Machine Learning Algorithms for Improving Black Box Optimization Solvers
by: Kimiaei, Morteza, et al.
Published: (2025)

Deep Relaxation of Controlled Stochastic Gradient Descent via Singular Perturbations
by: Bardi, Martino, et al.
Published: (2022)

Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimization
by: Dang, Duy-Minh, et al.
Published: (2025)

From Semi-Infinite Constraints to Structured Robust Policies: Optimal Gain Selection for Financial Systems
by: Hsieh, Chung-Han
Published: (2022)

Entropic Risk-Averse Generalized Momentum Methods
by: Can, Bugra, et al.
Published: (2022)

Optimistic Training and Convergence of Q-Learning -- Extended Version
by: Mehta, Prashant, et al.
Published: (2026)

An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems
by: Zeng, Zhijun, et al.
Published: (2025)

American Options Pricing under Heston Model via Curriculum Learning in Coupled PINNs
by: Rohan, et al.
Published: (2026)

Smart leverage? Rethinking the role of Leveraged Exchange Traded Funds in constructing portfolios to beat a benchmark
by: van Staden, Pieter, et al.
Published: (2024)

Anchored Likelihood-Ratio Geometry of Anonymous Shuffle Experiments: Exact Privacy Envelopes and Universal Low-Budget Design
by: Shvets, Alex
Published: (2026)

Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach
by: Liu, Jinjun, et al.
Published: (2026)

The exact region and an inequality between Chatterjee's and Spearman's rank correlations
by: Ansari, Jonathan, et al.
Published: (2025)

Deep neural networks can provably solve Bellman equations for Markov decision processes without the curse of dimensionality
by: Jentzen, Arnulf, et al.
Published: (2025)

Bayesian Conservative Policy Optimization (BCPO): A Novel Uncertainty-Calibrated Offline Reinforcement Learning with Credible Lower Bounds
by: Chatterjee, Debashis
Published: (2026)

Solving Optimal Execution Problems via In-Context Operator Networks
by: Meng, Tingwei, et al.
Published: (2025)

Stability and Sensitivity Analysis of Relative Temporal-Difference Learning: Extended Version
by: Sakha, Masoud S., et al.
Published: (2026)

A mean-field version of Bank-El Karoui's representation of stochastic processes
by: He, Xihao, et al.
Published: (2023)

Echo State Networks as State-Space Models: A Systems Perspective
by: Singh, Pradeep, et al.
Published: (2025)

Neural Policy Iteration for Stochastic Optimal Control: A Physics-Informed Approach
by: Kim, Yeongjong, et al.
Published: (2025)

Fourier Neural Network Approximation of Transition Densities in Finance
by: Du, Rong, et al.
Published: (2023)

Normal approximations for the multivariate inverse Gaussian distribution and asymmetric kernel smoothing on $d$-dimensional half-spaces
by: Belzile, Léo R., et al.
Published: (2022)

SAGA: A Sequence-Adaptive Generative Architecture for Multi-Horizon Probabilistic Forecasting with Adaptive Temporal Conformal Prediction
by: Lundström-Imanov, Gustav Olaf Yunus Laitinen-Fredriksson, et al.
Published: (2026)

Asynchronous Stochastic Approximation with Applications to Average-Reward Reinforcement Learning
by: Yu, Huizhen, et al.
Published: (2024)

Sample Average Approximation for Stochastic Programming with Equality Constraints
by: Lew, Thomas, et al.
Published: (2022)

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

Boosted generalized normal distributions: Integrating machine learning with operations knowledge
by: Gurlek, Ragip, et al.
Published: (2024)

A parametric approach to the estimation of convex risk functionals based on Wasserstein distance
by: Nendel, Max, et al.
Published: (2022)

An Empirical Comparison of Methods for Quantifying the Similarity of Categorical Datasets
by: Stolte, Marieke, et al.
Published: (2026)

An Empirical Comparison of Methods for Quantifying the Similarity of Numeric Datasets
by: Stolte, Marieke, et al.
Published: (2026)

Empirical Mode Decomposition and Graph Transformation of the MSCI World Index: A Multiscale Topological Analysis for Graph Neural Network Modeling
by: Riscos, Agustín M. de los, et al.
Published: (2025)

A Robust Model-Based Approach for Continuous-Time Policy Evaluation with Unknown Lévy Process Dynamics
by: Ye, Qihao, et al.
Published: (2025)