Imagen de Portada

Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Qu, Chengrui, Shi, Laixi, Panaganti, Kishan, You, Pengcheng, Wierman, Adam
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Distributionally Robust Constrained Reinforcement Learning under Strong Duality
por: Zhang, Zhengfei, et al.
Publicado: (2024)

Model-Free Robust $ϕ$-Divergence Reinforcement Learning Using Both Offline and Online Data
por: Panaganti, Kishan, et al.
Publicado: (2024)

KL-regularization Itself is Differentially Private in Bandits and RLHF
por: Zhang, Yizhou, et al.
Publicado: (2025)

Tractable Equilibrium Computation in Markov Games through Risk Aversion
por: Mazumdar, Eric, et al.
Publicado: (2024)

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty
por: Shi, Laixi, et al.
Publicado: (2024)

Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulation
por: Gu, Shangding, et al.
Publicado: (2024)

Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization
por: Qu, Chengrui, et al.
Publicado: (2026)

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

Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
por: Lu, Chenbei, et al.
Publicado: (2024)

Breaking the Curse of Multiagency in Robust Multi-Agent Reinforcement Learning
por: Shi, Laixi, et al.
Publicado: (2024)

Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity
por: Yang, Yingxuan, et al.
Publicado: (2026)

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
por: Shi, Laixi, et al.
Publicado: (2022)

Risk-Averse Total-Reward Reinforcement Learning
por: Su, Xihong, et al.
Publicado: (2025)

Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning
por: Gu, Shangding, et al.
Publicado: (2025)

Group Distributionally Robust Optimization-Driven Reinforcement Learning for LLM Reasoning
por: Panaganti, Kishan, et al.
Publicado: (2026)

Conceptual Belief-Informed Reinforcement Learning
por: Gu, Xingrui, et al.
Publicado: (2024)

Every Question Has Its Own Value: Reinforcement Learning with Explicit Human Values
por: Yu, Dian, et al.
Publicado: (2025)

Can LLMs Guide Their Own Exploration? Gradient-Guided Reinforcement Learning for LLM Reasoning
por: Liang, Zhenwen, et al.
Publicado: (2025)

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

Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency
por: Cai, Qi, et al.
Publicado: (2022)

Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes
por: Wang, He, et al.
Publicado: (2024)

Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices
por: Woo, Jiin, et al.
Publicado: (2024)

Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
por: Gai, Jingchu, et al.
Publicado: (2026)

Anytime-Competitive Reinforcement Learning with Policy Prior
por: Yang, Jianyi, et al.
Publicado: (2023)

Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
por: Liu, Zhishuai, et al.
Publicado: (2024)

Off-Policy Evaluation Using Information Borrowing and Context-Based Switching
por: Dasgupta, Sutanoy, et al.
Publicado: (2021)

Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach
por: Lu, Chenbei, et al.
Publicado: (2025)

T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamics
por: Shen, Zeyu, et al.
Publicado: (2026)

Provable Domain Adaptation for Offline Reinforcement Learning with Limited Samples
por: Chen, Weiqin, et al.
Publicado: (2024)

Provably Sample-Efficient Robust Reinforcement Learning with Average Reward
por: Roch, Zachary, et al.
Publicado: (2025)

Robust LLM Alignment via Distributionally Robust Direct Preference Optimization
por: Xu, Zaiyan, et al.
Publicado: (2025)

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

Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review
por: Hassani, Hossein, et al.
Publicado: (2024)

SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
por: Zhang, Shuai, et al.
Publicado: (2024)

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model
por: Shi, Laixi, et al.
Publicado: (2023)

Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
por: Cheng, Ziheng, et al.
Publicado: (2025)

Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
por: Kong, Lingkai, et al.
Publicado: (2025)

BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
por: Lin, Haohong, et al.
Publicado: (2024)

Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes
por: Vora, Kevin, et al.
Publicado: (2025)

SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
por: As, Yarden, et al.
Publicado: (2025)