Provable Multi-Task Reinforcement Learning: A Representation Learning Framework with Low Rank Rewards
Fuente:
arXiv
Saved in:
| Main Authors: | Guo, Yaoze, Moothedath, Shana |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning
by: Kang, Donghwa, et al.
Published: (2025)
by: Kang, Donghwa, et al.
Published: (2025)
Learning Shared Representations for Multi-Task Linear Bandits
by: Lin, Jiabin, et al.
Published: (2026)
by: Lin, Jiabin, et al.
Published: (2026)
Multi-Task Representation Learning for Conservative Linear Bandits
by: Lin, Jiabin, et al.
Published: (2026)
by: Lin, Jiabin, et al.
Published: (2026)
Byzantine Resilient Federated Multi-Task Representation Learning
by: Le, Tuan, et al.
Published: (2025)
by: Le, Tuan, et al.
Published: (2025)
Diffusion-based Decentralized Federated Multi-Task Representation Learning
by: Kang, Donghwa, et al.
Published: (2025)
by: Kang, Donghwa, et al.
Published: (2025)
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
by: Lin, Jiabin, et al.
Published: (2024)
by: Lin, Jiabin, et al.
Published: (2024)
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints
by: Lin, Jiabin, et al.
Published: (2024)
by: Lin, Jiabin, et al.
Published: (2024)
Federated Learning for Heterogeneous Bandits with Unobserved Contexts
by: Lin, Jiabin, et al.
Published: (2023)
by: Lin, Jiabin, et al.
Published: (2023)
Decentralized Communication-Efficient Multi-Task Representation Learning
by: Moothedath, Shana, et al.
Published: (2022)
by: Moothedath, Shana, et al.
Published: (2022)
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
by: Jose, Sharu Theresa, et al.
Published: (2024)
by: Jose, Sharu Theresa, et al.
Published: (2024)
Neural Contextual Bandits Under Delayed Feedback Constraints
by: Moghimi, Mohammadali, et al.
Published: (2025)
by: Moghimi, Mohammadali, et al.
Published: (2025)
Provable Meta-Learning with Low-Rank Adaptations
by: Block, Jacob L., et al.
Published: (2024)
by: Block, Jacob L., et al.
Published: (2024)
A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning
by: Rozada, Sergio, et al.
Published: (2025)
by: Rozada, Sergio, et al.
Published: (2025)
Provably Sample-Efficient Robust Reinforcement Learning with Average Reward
by: Roch, Zachary, et al.
Published: (2025)
by: Roch, Zachary, et al.
Published: (2025)
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation
by: Bai, Yitao, et al.
Published: (2025)
by: Bai, Yitao, et al.
Published: (2025)
Provable Exactness for Asymmetric Low-Rank SDP Learning
by: Hu, Enliang
Published: (2018)
by: Hu, Enliang
Published: (2018)
Multi Task Inverse Reinforcement Learning for Common Sense Reward
by: Glazer, Neta, et al.
Published: (2024)
by: Glazer, Neta, et al.
Published: (2024)
Shift Before You Learn: Enabling Low-Rank Representations in Reinforcement Learning
by: Dubail, Bastien, et al.
Published: (2025)
by: Dubail, Bastien, et al.
Published: (2025)
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces
by: Kar, Avik, et al.
Published: (2024)
by: Kar, Avik, et al.
Published: (2024)
Provable Reward-Agnostic Preference-Based Reinforcement Learning
by: Zhan, Wenhao, et al.
Published: (2023)
by: Zhan, Wenhao, et al.
Published: (2023)
Provable Low-Frequency Bias of In-Context Learning of Representations
by: Yang, Yongyi, et al.
Published: (2025)
by: Yang, Yongyi, et al.
Published: (2025)
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
by: Collins, Liam, et al.
Published: (2023)
by: Collins, Liam, et al.
Published: (2023)
Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis
by: Li, Hongkang, et al.
Published: (2024)
by: Li, Hongkang, et al.
Published: (2024)
Reward-Aware Proto-Representations in Reinforcement Learning
by: Tse, Hon Tik, et al.
Published: (2025)
by: Tse, Hon Tik, et al.
Published: (2025)
Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences
by: Dimitriadis, Nikolaos, et al.
Published: (2024)
by: Dimitriadis, Nikolaos, et al.
Published: (2024)
Provably Efficient Reward Transfer in Reinforcement Learning with Discrete Markov Decision Processes
by: Vora, Kevin, et al.
Published: (2025)
by: Vora, Kevin, et al.
Published: (2025)
Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
by: Zhang, Hongming, et al.
Published: (2023)
by: Zhang, Hongming, et al.
Published: (2023)
Provably Efficient Interactive-Grounded Learning with Personalized Reward
by: Zhang, Mengxiao, et al.
Published: (2024)
by: Zhang, Mengxiao, et al.
Published: (2024)
MoRE: A Mixture of Low-Rank Experts for Adaptive Multi-Task Learning
by: Zhang, Dacao, et al.
Published: (2025)
by: Zhang, Dacao, et al.
Published: (2025)
Beyond Task Diversity: Provable Representation Transfer for Sequential Multi-Task Linear Bandits
by: Duong, Thang, et al.
Published: (2025)
by: Duong, Thang, et al.
Published: (2025)
Centralized Reward Agent for Knowledge Sharing and Transfer in Multi-Task Reinforcement Learning
by: Ma, Haozhe, et al.
Published: (2024)
by: Ma, Haozhe, et al.
Published: (2024)
The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed Rewards
by: Chen, Xi, et al.
Published: (2024)
by: Chen, Xi, et al.
Published: (2024)
Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors
by: Shehab, Mohamad Louai, et al.
Published: (2025)
by: Shehab, Mohamad Louai, et al.
Published: (2025)
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework
by: Deng, Yongxin, et al.
Published: (2024)
by: Deng, Yongxin, et al.
Published: (2024)
R^3: Replay, Reflection, and Ranking Rewards for LLM Reinforcement Learning
by: Jiang, Zhizheng, et al.
Published: (2026)
by: Jiang, Zhizheng, et al.
Published: (2026)
Towards Provable Emergence of In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2025)
by: Wang, Jiuqi, et al.
Published: (2025)
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
by: Chen, Ying-Tu, et al.
Published: (2026)
by: Chen, Ying-Tu, et al.
Published: (2026)
Provably Efficient Exploration in Reward Machines with Low Regret
by: Bourel, Hippolyte, et al.
Published: (2024)
by: Bourel, Hippolyte, et al.
Published: (2024)
On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning
by: Freihaut, Till, et al.
Published: (2024)
by: Freihaut, Till, et al.
Published: (2024)
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning
by: Yang, Yaming, et al.
Published: (2024)
by: Yang, Yaming, et al.
Published: (2024)
Similar Items
-
Beyond Centralization: Provable Communication Efficient Decentralized Multi-Task Learning
by: Kang, Donghwa, et al.
Published: (2025) -
Learning Shared Representations for Multi-Task Linear Bandits
by: Lin, Jiabin, et al.
Published: (2026) -
Multi-Task Representation Learning for Conservative Linear Bandits
by: Lin, Jiabin, et al.
Published: (2026) -
Byzantine Resilient Federated Multi-Task Representation Learning
by: Le, Tuan, et al.
Published: (2025) -
Diffusion-based Decentralized Federated Multi-Task Representation Learning
by: Kang, Donghwa, et al.
Published: (2025)