Copertina

Regret Analysis of Unichain Average Reward Constrained MDPs with General Parameterization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Satheesh, Anirudh, Aggarwal, Vaneet
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

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

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

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

Distributionally Robust Self Paced Curriculum Reinforcement Learning
di: Satheesh, Anirudh, et al.
Pubblicazione: (2025)

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

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

Provably Efficient Algorithms for S- and Non-Rectangular Robust 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)

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

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)

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

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

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

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

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

Sample Complexity Analysis for Constrained Bilevel Reinforcement Learning
di: Saxena, Naman, et al.
Pubblicazione: (2026)

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

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

A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
di: Satheesh, Anirudh, et al.
Pubblicazione: (2025)

Unichain and Aperiodicity are Sufficient for Asymptotic Optimality of Average-Reward Restless Bandits
di: Hong, Yige, 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)

Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
di: Moradipari, Ahmadreza, et al.
Pubblicazione: (2023)

Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model
di: Malusare, Aditya, 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)

Two-Timescale Critic-Actor for Average Reward MDPs with Function Approximation
di: Panda, Prashansa, et al.
Pubblicazione: (2024)

Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
di: Provodin, Danil, et al.
Pubblicazione: (2024)

BAGEL: Projection-Free Algorithm for Adversarially Constrained Online Convex Optimization
di: Lu, Yiyang, 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)

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

Logarithmic Regret of Exploration in Average Reward Markov Decision Processes
di: Boone, Victor, et al.
Pubblicazione: (2025)

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

Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
di: Zurek, Matthew, et al.
Pubblicazione: (2024)

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

Solving Robust MDPs through No-Regret Dynamics
di: Guha, Etash Kumar
Pubblicazione: (2023)

Eluder-based Regret for Stochastic Contextual MDPs
di: Levy, Orin, et al.
Pubblicazione: (2022)