Copertina

Order-Optimal Regret with Novel Policy Gradient Approaches in Infinite-Horizon Average Reward MDPs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ganesh, Swetha, Mondal, Washim Uddin, Aggarwal, Vaneet
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes
di: Bai, Qinbo, et al.
Pubblicazione: (2023)

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

A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach
di: Ganesh, Swetha, et al.
Pubblicazione: (2024)

Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
di: Ganesh, Swetha, et al.
Pubblicazione: (2025)

Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes
di: Mondal, Washim Uddin, et al.
Pubblicazione: (2023)

Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
di: Xu, Yang, et al.
Pubblicazione: (2025)

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

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

Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning
di: Xu, Yang, et al.
Pubblicazione: (2025)

Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms
di: Aggarwal, Vaneet, et al.
Pubblicazione: (2024)

Regret Analysis of Unichain Average Reward Constrained MDPs with General Parameterization
di: Satheesh, Anirudh, et al.
Pubblicazione: (2026)

Sample-Efficient Constrained Reinforcement Learning with General Parameterization
di: Mondal, Washim Uddin, et al.
Pubblicazione: (2024)

Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
di: Ganguly, Bhargav, et al.
Pubblicazione: (2023)

Efficient $Q$-Learning and Actor-Critic Methods for Robust Average Reward Reinforcement Learning
di: Xu, Yang, et al.
Pubblicazione: (2025)

Primal-Only Actor Critic Algorithm for Robust Constrained Average Cost MDPs
di: Satheesh, Anirudh, et al.
Pubblicazione: (2025)

Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)
di: Mondal, Washim Uddin, et al.
Pubblicazione: (2022)

Breaking the Bias Barrier in Concave Multi-Objective Reinforcement Learning
di: Ganesh, Swetha, et al.
Pubblicazione: (2026)

Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
di: Ganesh, Swetha, et al.
Pubblicazione: (2024)

Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs via Approximation by Discounted-Reward MDPs
di: Hong, Kihyuk, et al.
Pubblicazione: (2024)

Optimal Variance-Dependent Regret Bounds for Infinite-Horizon MDPs
di: Zamir, Guy, et al.
Pubblicazione: (2026)

Achieving Tractable Minimax Optimal Regret in Average Reward MDPs
di: Boone, Victor, et al.
Pubblicazione: (2024)

Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
di: Chae, Woojin, et al.
Pubblicazione: (2024)

A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
di: Hong, Kihyuk, et al.
Pubblicazione: (2025)

Match or Replay: Self Imitating Proximal Policy Optimization
di: Chaudhary, Gaurav, et al.
Pubblicazione: (2026)

Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
di: Xu, Yang, et al.
Pubblicazione: (2025)

Hierarchical Deep Counterfactual Regret Minimization
di: Chen, Jiayu, et al.
Pubblicazione: (2023)

Non-Rectangular Average-Reward Robust MDPs: Optimal Policies and Their Transient Values
di: Wang, Shengbo, et al.
Pubblicazione: (2026)

Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning
di: Kar, Avik, et al.
Pubblicazione: (2024)

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

The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis
di: Zurek, Matthew, et al.
Pubblicazione: (2024)

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

Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles
di: Patel, Bhrij, et al.
Pubblicazione: (2024)

Order-Optimal Sample Complexity of Rectified Flows
di: Sahoo, Hari Krishna, et al.
Pubblicazione: (2026)

Span-Based Optimal Sample Complexity for Average Reward MDPs
di: Zurek, Matthew, et al.
Pubblicazione: (2023)

Joint Optimization of Multi-Objective Reinforcement Learning with Policy Gradient Based Algorithm
di: Bai, Qinbo, et al.
Pubblicazione: (2021)

Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs
di: Zhang, Junkai, et al.
Pubblicazione: (2023)

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

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

Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
di: Levy, Orin, et al.
Pubblicazione: (2026)

Persistent-Transient Policy Evaluation for Markov Chains via Minimal Peripheral Quotients
di: Xu, Yang, et al.
Pubblicazione: (2026)