Imagen de Portada

Enhancing Sample Efficiency in Multi-Agent RL with Uncertainty Quantification and Selective Exploration

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Danino, Tom, Shimkin, Nahum
Formato:	Preprint
Publicado:	2025
Materias:	Systems and Control Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
por: Gerstgrasser, Matthias, et al.
Publicado: (2023)

SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning
por: Xu, Jianye, et al.
Publicado: (2024)

Learning Surrogate LPV State-Space Models with Uncertainty Quantification
por: Olucha, E. Javier, et al.
Publicado: (2026)

Uncertainty Quantification of Data-Driven Output Predictors in the Output Error Setting
por: Kaviani, Farzan, et al.
Publicado: (2024)

xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
por: Kececi, Ertugrul, et al.
Publicado: (2026)

Smart Exploration in Reinforcement Learning using Bounded Uncertainty Models
por: van Hulst, J. S., et al.
Publicado: (2025)

Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
por: Ma, Yiran, et al.
Publicado: (2026)

Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents
por: Bejarano, Federico Pizarro, et al.
Publicado: (2024)

Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
por: Jiang, Zhanhong, et al.
Publicado: (2025)

Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm
por: Qiao, Ting, et al.
Publicado: (2024)

RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
por: Dwivedi, Anmol, et al.
Publicado: (2024)

Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration
por: Shi, Zhaoyang
Publicado: (2025)

Uncertainty Quantification for Data-Driven Machine Learning Models in Nuclear Engineering Applications: Where We Are and What Do We Need?
por: Wu, Xu, et al.
Publicado: (2025)

LLM-Driven Composite Neural Architecture Search for Multi-Source RL State Encoding
por: Yu, Yu, et al.
Publicado: (2025)

Zono-Conformal Prediction: Zonotope-Based Uncertainty Quantification for Regression and Classification Tasks
por: Lützow, Laura, et al.
Publicado: (2025)

Balancing Specialization and Centralization: A Multi-Agent Reinforcement Learning Benchmark for Sequential Industrial Control
por: Maus, Tom, et al.
Publicado: (2025)

EcoFair-CH-MARL: Scalable Constrained Hierarchical Multi-Agent RL with Real-Time Emission Budgets and Fairness Guarantees
por: Alqithami, Saad
Publicado: (2026)

Optimization and Learning in Open Multi-Agent Systems
por: Deplano, Diego, et al.
Publicado: (2025)

Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration
por: Baghi, Amir, et al.
Publicado: (2025)

IMAGINE: Intelligent Multi-Agent Godot-based Indoor Networked Exploration
por: Leite, Tiago, et al.
Publicado: (2026)

ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors
por: Rap, Jake, et al.
Publicado: (2025)

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
por: Zolman, Nicholas, et al.
Publicado: (2024)

Enhancing Reinforcement Learning Agents with Local Guides
por: Daoudi, Paul, et al.
Publicado: (2024)

Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency
por: Zhang, Qixin, et al.
Publicado: (2025)

Neural Spline Operators for Risk Quantification in Stochastic Systems
por: Wang, Zhuoyuan, et al.
Publicado: (2025)

Physics-informed RL for Maximal Safety Probability Estimation
por: Hoshino, Hikaru, et al.
Publicado: (2024)

The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
por: Muehlebach, Michael, et al.
Publicado: (2025)

FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets
por: Jadhav, Shrenik, et al.
Publicado: (2025)

Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning
por: Zhang, Runyu, et al.
Publicado: (2025)

Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks
por: Doostmohammadian, Mohammadreza, et al.
Publicado: (2024)

Multi-Agent Stage-wise Conservative Linear Bandits
por: Afsharrad, Amirhossein, et al.
Publicado: (2025)

Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits
por: Zhu, Jingxuan, et al.
Publicado: (2021)

Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agents
por: Banker, Thomas, et al.
Publicado: (2025)

Embedding Safety into RL: A New Take on Trust Region Methods
por: Milosevic, Nikola, et al.
Publicado: (2024)

Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms
por: Nilsen, Marcus Binder, et al.
Publicado: (2026)

Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective
por: Syed, Shahbaz P Qadri, et al.
Publicado: (2025)

Multi-Agent Hybrid SAC for Joint SS-DSA in CRNs
por: Nickel, David R., et al.
Publicado: (2024)

RL-TIME: Reinforcement Learning-based Task Replication in Multicore Embedded Systems
por: Siyadatzadeh, Roozbeh, et al.
Publicado: (2025)

A semi-centralized multi-agent RL framework for efficient irrigation scheduling
por: Agyeman, Bernard T., et al.
Publicado: (2024)

An LP-based Sampling Policy for Multi-Armed Bandits with Side-Observations and Stochastic Availability
por: Soni, Ashutosh, et al.
Publicado: (2026)