:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Tan, Kevin, Fan, Wei, Wei, Yuting
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2408.04526
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
by: Li, Gen, et al.
Published: (2020)

Actor-Critics Can Achieve Optimal Sample Efficiency
by: Tan, Kevin, et al.
Published: (2025)

Statistical Inference under Adaptive Sampling with LinUCB
by: Fan, Wei, et al.
Published: (2025)

Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions
by: Mhammedi, Zakaria
Published: (2024)

Offline-Online Reinforcement Learning for Linear Mixture MDPs
by: Zhang, Zhongjun, et al.
Published: (2026)

Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
by: Maran, Davide, et al.
Published: (2024)

Sample Complexity Characterization for Linear Contextual MDPs
by: Deng, Junze, et al.
Published: (2024)

Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs
by: Huang, Ruiquan, et al.
Published: (2026)

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

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs
by: John, Philips George, et al.
Published: (2024)

CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
by: Mak, Hei Yi, et al.
Published: (2024)

A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs
by: Hong, Kihyuk, et al.
Published: (2024)

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)

Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
by: Maran, Davide, et al.
Published: (2024)

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

No-Regret Reinforcement Learning in Smooth MDPs
by: Maran, Davide, et al.
Published: (2024)

Reinforcement Learning from Adversarial Preferences in Tabular MDPs
by: Tsuchiya, Taira, et al.
Published: (2025)

Imitation Learning in Discounted Linear MDPs without exploration assumptions
by: Viano, Luca, et al.
Published: (2024)

Settling the Sample Complexity of Model-Based Offline Reinforcement Learning
by: Li, Gen, et al.
Published: (2022)

Statistical Inference for Temporal Difference Learning with Linear Function Approximation
by: Wu, Weichen, et al.
Published: (2024)

AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
by: Grigsby, Jake, et al.
Published: (2024)

Reinforcement Learning for Exponential Utility: Algorithms and Convergence in Discounted MDPs
by: Thoppe, Gugan, et al.
Published: (2026)

Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs
by: Hung, Wei, et al.
Published: (2025)

Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments
by: Kaya, Ege C., et al.
Published: (2026)

Breaking the Finite-Sample Barrier in Entropy Coupling
by: Asoodeh, Shahab, et al.
Published: (2026)

Statistical and Algorithmic Foundations of Reinforcement Learning
by: Chi, Yuejie, et al.
Published: (2025)

Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
by: Lee, Joongkyu, et al.
Published: (2024)

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

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

STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs
by: Dong, Peijie, et al.
Published: (2024)

Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents
by: Sun, Chung-En, et al.
Published: (2024)

Convex Is Back: Solving Belief MDPs With Convexity-Informed Deep Reinforcement Learning
by: Koutas, Daniel, et al.
Published: (2025)

Provable Offline Reinforcement Learning for Structured Cyclic MDPs
by: Lee, Kyungbok, et al.
Published: (2026)

Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
by: Li, Long-Fei, et al.
Published: (2024)

Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
by: Cassel, Asaf, et al.
Published: (2024)

Is Pure Exploitation Sufficient in Exogenous MDPs with Linear Function Approximation?
by: Liang, Hao, et al.
Published: (2026)

Reinforcement Learning in MDPs with Information-Ordered Policies
by: Zhang, Zhongjun, et al.
Published: (2025)

FIS-DiT: Breaking the Few-Step Video Inference Barrier via Training-Free Frame Interleaved Sparsity
by: Tang, Jian, et al.
Published: (2026)

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