Cover Image

Risk-sensitive Markov Decision Process and Learning under General Utility Functions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Zhengqi, Xu, Renyuan
Format:	Preprint
Published:	2023
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

Inference of Utilities and Time Preference in Sequential Decision-Making
by: Cao, Haoyang, et al.
Published: (2024)

Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes
by: Jin, Hanqing, et al.
Published: (2025)

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
by: Xu, Mingyuan, et al.
Published: (2026)

Non-stationary and Varying-discounting Markov Decision Processes for Reinforcement Learning
by: Chen, Zhizuo, et al.
Published: (2025)

Online Reinforcement Learning in Markov Decision Process Using Linear Programming
by: Leon, Vincent, et al.
Published: (2023)

Reinforcement Learning with Function Approximation for Non-Markov Processes
by: Kara, Ali Devran
Published: (2026)

Fast Policy Learning for Linear Quadratic Control with Entropy Regularization
by: Guo, Xin, et al.
Published: (2023)

Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
by: Mazumdar, Abhijit, et al.
Published: (2024)

On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes
by: Wan, Yi, et al.
Published: (2024)

Weakly Time-Coupled Approximation of Markov Decision Processes
by: Soheili, Negar, et al.
Published: (2026)

Online Markov Decision Processes with Terminal Law Constraints
by: Moreno, Bianca Marin, et al.
Published: (2026)

Optimal Sample Complexity for Average Reward Markov Decision Processes
by: Wang, Shengbo, et al.
Published: (2023)

Flipping-based Policy for Chance-Constrained Markov Decision Processes
by: Shen, Xun, et al.
Published: (2024)

Achieving Instance-dependent Sample Complexity for Constrained Markov Decision Process
by: Jiang, Jiashuo, et al.
Published: (2024)

Efficient Algorithms for Robust Markov Decision Processes with $s$-Rectangular Ambiguity Sets
by: Ho, Chin Pang, et al.
Published: (2026)

Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators
by: Han, Yinbin, et al.
Published: (2023)

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence
by: Han, Yinbin, et al.
Published: (2024)

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
by: Wang, Shengbo, et al.
Published: (2025)

Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
by: Neufeld, Ariel, et al.
Published: (2022)

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
by: Kitamura, Toshinori, et al.
Published: (2024)

Bayesian Ambiguity Contraction-based Adaptive Robust Markov Decision Processes for Adversarial Surveillance Missions
by: Choi, Jimin, et al.
Published: (2025)

Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
by: Zhang, Weitong, et al.
Published: (2021)

Federated Calculation of the Free-Support Transportation Barycenter by Single-Loop Dual Decomposition
by: Lin, Zhengqi, et al.
Published: (2025)

Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes
by: Zeng, Sihan, et al.
Published: (2021)

Scalable Bi-causal Optimal Transport via KL Relaxation and Policy Gradients
by: Cao, Haoyang, et al.
Published: (2026)

Solving Sparse \& High-Dimensional-Output Regression via Compression
by: Li, Renyuan, et al.
Published: (2024)

Risk-sensitive control as inference with Rényi divergence
by: Ito, Kaito, et al.
Published: (2024)

Decision Making under Costly Sequential Information Acquisition: the Paradigm of Reversible and Irreversible Decisions
by: Xu, Renyuan, et al.
Published: (2023)

Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
by: Chi, Wenhao, et al.
Published: (2026)

Policy Mirror Descent with Temporal Difference Learning: Sample Complexity under Online Markov Data
by: Li, Wenye, et al.
Published: (2025)

Approximate Linear Programming for Decentralized Policy Iteration in Cooperative Multi-agent Markov Decision Processes
by: Mandal, Lakshmi, et al.
Published: (2023)

Sufficient Decision Proxies for Decision-Focused Learning
by: Schutte, Noah, et al.
Published: (2025)

An Integrated Transportation Distance Between Kernels and Approximate Dynamic Risk Evaluation in Markov Systems
by: Lin, Zhengqi, et al.
Published: (2023)

Schrödinger bridge for generative AI: Soft-constrained formulation and convergence analysis
by: Ma, Jin, et al.
Published: (2025)

Learning Cut Generating Functions for Integer Programming
by: Cheng, Hongyu, et al.
Published: (2024)

Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity
by: Wang, Han, et al.
Published: (2023)

Gradient-Variation Online Learning under Generalized Smoothness
by: Xie, Yan-Feng, et al.
Published: (2024)

Markov Decision Processes with Value-at-Risk Criterion
by: Xia, Li, et al.
Published: (2025)

$k$-Submodular Interdiction Problems under Distributional Risk-Receptiveness and Robustness: Application to Machine Learning
by: Park, Seonghun, et al.
Published: (2024)

A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
by: Sadana, Utsav, et al.
Published: (2023)