Copertina

Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Lin, Jiabin, Moothedath, Shana
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Multiagent Systems
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Learning Shared Representations for Multi-Task Linear Bandits
di: Lin, Jiabin, et al.
Pubblicazione: (2026)

Multi-Task Representation Learning for Conservative Linear Bandits
di: Lin, Jiabin, et al.
Pubblicazione: (2026)

Federated Learning for Heterogeneous Bandits with Unobserved Contexts
di: Lin, Jiabin, et al.
Pubblicazione: (2023)

Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits
di: Lin, Jiabin, et al.
Pubblicazione: (2024)

Cooperative Multi-Agent Constrained Stochastic Linear Bandits
di: Afsharrad, Amirhossein, et al.
Pubblicazione: (2024)

Distributed Networked Multi-task Learning
di: Hong, Lingzhou, et al.
Pubblicazione: (2024)

The Bandit Whisperer: Communication Learning for Restless Bandits
di: Zhao, Yunfan, et al.
Pubblicazione: (2024)

In-Context Curiosity: Distilling Exploration for Decision-Pretrained Transformers on Bandit Tasks
di: Yang, Huitao, et al.
Pubblicazione: (2025)

Distributed Continual Learning
di: Le, Long, et al.
Pubblicazione: (2024)

Curriculum Imitation Learning of Distributed Multi-Robot Policies
di: Roche, Jesús, et al.
Pubblicazione: (2025)

Adversarial Bandit over Bandits: Hierarchical Bandits for Online Configuration Management
di: Avin, Chen, et al.
Pubblicazione: (2025)

Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler Mitigation
di: Wang, Baoqian, et al.
Pubblicazione: (2022)

Sample-Efficient Distributionally Robust Multi-Agent Reinforcement Learning via Online Interaction
di: Farhat, Zain Ulabedeen, et al.
Pubblicazione: (2025)

Distributed Detection of Adversarial Attacks in Multi-Agent Reinforcement Learning with Continuous Action Space
di: Kazari, Kiarash, et al.
Pubblicazione: (2025)

Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit
di: Xu, Mengfan, et al.
Pubblicazione: (2024)

ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization
di: Bui, The Viet, et al.
Pubblicazione: (2024)

UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing
di: Yang, Yubo, et al.
Pubblicazione: (2025)

Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
di: Jose, Sharu Theresa, et al.
Pubblicazione: (2024)

Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysis
di: Paschalidis, Phevos, et al.
Pubblicazione: (2024)

Context-aware Communication for Multi-agent Reinforcement Learning
di: Li, Xinran, et al.
Pubblicazione: (2023)

Distributed Adaptive Learning Under Communication Constraints
di: Carpentiero, Marco, et al.
Pubblicazione: (2021)

Distributed Area Coverage with High Altitude Balloons Using Multi-Agent Reinforcement Learning
di: Haroon, Adam, et al.
Pubblicazione: (2025)

Anomaly Detection in Networked Bandits
di: Cheng, Xiaotong, et al.
Pubblicazione: (2025)

A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health
di: Behari, Nikhil, et al.
Pubblicazione: (2024)

Spatiotemporal Attention-Augmented Inverse Reinforcement Learning for Multi-Agent Task Allocation
di: Yin, Huilin, et al.
Pubblicazione: (2025)

Learning to Coordinate: Distributed Meta-Trajectory Optimization Via Differentiable ADMM-DDP
di: Wang, Bingheng, et al.
Pubblicazione: (2025)

Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning
di: Duan, Wenchang, et al.
Pubblicazione: (2025)

Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox
di: Yin, Qiyue, et al.
Pubblicazione: (2022)

MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments
di: Sawaika, Abhishek, et al.
Pubblicazione: (2026)

When Parallelism Pays Off: Cohesion-Aware Task Partitioning for Multi-Agent Coding
di: Yang, Xu, et al.
Pubblicazione: (2026)

Combining Diverse Information for Coordinated Action: Stochastic Bandit Algorithms for Heterogeneous Agents
di: Gordon, Lucia, et al.
Pubblicazione: (2024)

DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints
di: Hassan, Raheeb, et al.
Pubblicazione: (2023)

Distributed Differentiable Dynamic Game for Multi-robot Coordination
di: Zhou, Yizhi, et al.
Pubblicazione: (2022)

Distributionally Robust Inverse Reinforcement Learning for Identifying Multi-Agent Coordinated Sensing
di: Snow, Luke, et al.
Pubblicazione: (2024)

Near Optimal Best Arm Identification for Clustered Bandits
di: Yash, et al.
Pubblicazione: (2025)

Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
di: Hanna, Osama A., et al.
Pubblicazione: (2023)

cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending
di: Satheesh, Anirudh, et al.
Pubblicazione: (2025)

Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
di: Jing, Gangshan, et al.
Pubblicazione: (2022)

Context Learning for Multi-Agent Discussion
di: Hua, Xingyuan, et al.
Pubblicazione: (2026)

On Collaboration in Distributed Parameter Estimation with Resource Constraints
di: Chen, Yu-Zhen Janice, et al.
Pubblicazione: (2023)