Cover Image

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Runze, Yu, Yue, Zhu, Adams Yiyue, Yang, Chen, Zhou, Dongruo
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Uncertainty-Aware Reward-Free Exploration with General Function Approximation
by: Zhang, Junkai, et al.
Published: (2024)

Provable Zero-Shot Generalization in Offline Reinforcement Learning
by: Wang, Zhiyong, et al.
Published: (2025)

Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation
by: Zhao, Runze, et al.
Published: (2025)

Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning
by: Wang, Ruhan, et al.
Published: (2024)

CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing
by: Yang, Chen, et al.
Published: (2024)

More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
by: Ishfaq, Haque, et al.
Published: (2024)

Towards Differentially Private Reinforcement Learning with General Function Approximation
by: He, Yi, et al.
Published: (2026)

How to Provably Improve Return Conditioned Supervised Learning?
by: Liu, Zhishuai, et al.
Published: (2025)

Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
by: Cho, Taehyun, et al.
Published: (2024)

Continual Learning as Computationally Constrained Reinforcement Learning
by: Kumar, Saurabh, et al.
Published: (2023)

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

CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning
by: Alinejad, Mahyar, et al.
Published: (2026)

On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
by: Huang, Jiawei, et al.
Published: (2023)

Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning
by: Luo, Zhi, et al.
Published: (2024)

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

Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning
by: Zhu, Yuanyang, et al.
Published: (2024)

Continual Offline Reinforcement Learning via Diffusion-based Dual Generative Replay
by: Liu, Jinmei, et al.
Published: (2024)

Test-driven Reinforcement Learning in Continuous Control
by: Yu, Zhao, et al.
Published: (2025)

GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
by: Zhang, Yunan, et al.
Published: (2025)

GCHR : Goal-Conditioned Hindsight Regularization for Sample-Efficient Reinforcement Learning
by: Lei, Xing, et al.
Published: (2025)

Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
by: Wang, Zhiyong, et al.
Published: (2024)

Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning
by: Liu, Xin, et al.
Published: (2024)

Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following
by: Zhang, Kongcheng, et al.
Published: (2025)

Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency
by: Long, Jiangxuan, et al.
Published: (2025)

On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage
by: Liu, Haolin, et al.
Published: (2026)

Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
by: Rozada, Sergio, et al.
Published: (2022)

Sample Efficient Active Algorithms for Offline Reinforcement Learning
by: Roy, Soumyadeep, et al.
Published: (2026)

Goal Discovery with Causal Capacity for Efficient Reinforcement Learning
by: Yu, Yan, et al.
Published: (2025)

Model-Free Robust Reinforcement Learning with Sample Complexity Analysis
by: Wang, Yudan, et al.
Published: (2024)

On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond
by: Nguyen-Tang, Thanh, et al.
Published: (2024)

Speculative Sampling with Reinforcement Learning
by: Wang, Chenan, et al.
Published: (2026)

Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning
by: Zhu, Ting, et al.
Published: (2024)

Fourier Basis Mapping: A Time-Frequency Learning Framework for Time Series Forecasting
by: Yang, Runze, et al.
Published: (2025)

The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation
by: Golowich, Noah, et al.
Published: (2024)

Federated Self-Supervised Learning for Automatic Modulation Classification under Non-IID and Class-Imbalanced Data
by: Akram, Usman, et al.
Published: (2025)

Solving Continual Offline Reinforcement Learning with Decision Transformer
by: Huang, Kaixin, et al.
Published: (2024)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
by: Liu, Vincent, et al.
Published: (2023)

q-Learning in Continuous Time
by: Jia, Yanwei, et al.
Published: (2022)

Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations
by: Zuo, Bowen, et al.
Published: (2026)

Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
by: Huang, Jiayi, et al.
Published: (2023)