Cover Image

Confident Natural Policy Gradient for Local Planning in $q_π$-realizable Constrained MDPs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tian, Tian, Yang, Lin F., Szepesvári, Csaba
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPs
by: Zhong, Han, et al.
Published: (2021)

Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear $q^π$-Realizability and Concentrability
by: Tkachuk, Volodymyr, et al.
Published: (2024)

Ordering-based Conditions for Global Convergence of Policy Gradient Methods
by: Mei, Jincheng, et al.
Published: (2025)

LACONIC: Length-Aware Constrained Reinforcement Learning for LLM
by: Liu, Chang, et al.
Published: (2026)

Sharper Guarantees for Misspecified Kernelized Bandit Optimization
by: Maran, Davide, et al.
Published: (2026)

Rectifying Regression in Reinforcement Learning
by: Ayoub, Alex, et al.
Published: (2025)

Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation
by: Lin, Max Qiushi, et al.
Published: (2025)

Sharp analysis of linear ensemble sampling
by: Akhavan, Arya, et al.
Published: (2026)

Stochastic Gradient Succeeds for Bandits
by: Mei, Jincheng, et al.
Published: (2024)

Almost Free: Self-concordance in Natural Exponential Families and an Application to Bandits
by: Liu, Shuai, et al.
Published: (2024)

A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees
by: Kitamura, Toshinori, et al.
Published: (2024)

Near-Optimal Sample Complexity for Online Constrained MDPs
by: Liu, Chang, et al.
Published: (2026)

Convergence of Natural Policy Gradient for a Family of Infinite-State Queueing MDPs
by: Grosof, Isaac, et al.
Published: (2024)

Optimistic Actor-Critic with Parametric Policies for Linear Markov Decision Processes
by: Lin, Max Qiushi, et al.
Published: (2026)

Balancing optimism and pessimism in offline-to-online learning
by: Sentenac, Flore, et al.
Published: (2025)

Ensemble sampling for linear bandits: small ensembles suffice
by: Janz, David, et al.
Published: (2023)

Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity
by: Zhang, Runyu, et al.
Published: (2023)

Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs
by: Ding, Dongsheng, et al.
Published: (2023)

Sample Complexity Bounds for Linear Constrained MDPs with a Generative Model
by: Liu, Xingtu, et al.
Published: (2025)

Near-Optimal Sample Complexity Bounds for Constrained Average-Reward MDPs
by: Wei, Yukuan, et al.
Published: (2025)

Exploration via linearly perturbed loss minimisation
by: Janz, David, et al.
Published: (2023)

Policy Gradient in Robust MDPs with Global Convergence Guarantee
by: Wang, Qiuhao, et al.
Published: (2022)

Towards Principled, Practical Policy Gradient for Bandits and Tabular MDPs
by: Lu, Michael, et al.
Published: (2024)

Stochastic Gradient Descent for Gaussian Processes Done Right
by: Lin, Jihao Andreas, et al.
Published: (2023)

Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
by: György, András, et al.
Published: (2025)

Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization
by: Stradi, Francesco Emanuele, et al.
Published: (2024)

Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm
by: Bai, Qinbo, et al.
Published: (2024)

Why Policy Gradient Algorithms Work for Undiscounted Total-Reward MDPs
by: Lee, Jongmin, et al.
Published: (2025)

Time-Constrained Robust MDPs
by: Zouitine, Adil, et al.
Published: (2024)

Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs
by: Mondal, Washim Uddin, et al.
Published: (2024)

Learning to Reason Efficiently with Discounted Reinforcement Learning
by: Ayoub, Alex, et al.
Published: (2025)

Eluder dimension: localise it!
by: Bakhtiari, Alireza, et al.
Published: (2026)

Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits
by: Liu, Shuai, et al.
Published: (2026)

Truly No-Regret Learning in Constrained MDPs
by: Müller, Adrian, et al.
Published: (2024)

Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs
by: Lu, Michael, et al.
Published: (2026)

Global Convergence of Average Reward Constrained MDPs with Neural Critic and General Policy Parameterization
by: Satheesh, Anirudh, et al.
Published: (2026)

Regret Minimization via Saddle Point Optimization
by: Kirschner, Johannes, et al.
Published: (2024)

To Believe or Not to Believe Your LLM
by: Yadkori, Yasin Abbasi, et al.
Published: (2024)

Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs
by: Ganesh, Swetha, et al.
Published: (2024)

Efficient Policy Optimization in Robust Constrained MDPs with Iteration Complexity Guarantees
by: Ganguly, Sourav, et al.
Published: (2025)