Copertina

DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hassan, Raheeb, Wadith, K. M. Shadman, Rashid, Md. Mamun or, Khan, Md. Mosaddek
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Multiagent Systems Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Optimizing UAV-UGV Coalition Operations: A Hybrid Clustering and Multi-Agent Reinforcement Learning Approach for Path Planning in Obstructed Environment
di: Brotee, Shamyo, et al.
Pubblicazione: (2024)

Enhancing Heterogeneous Multi-Agent Cooperation in Decentralized MARL via GNN-driven Intrinsic Rewards
di: Monon, Jahir Sadik, et al.
Pubblicazione: (2024)

Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System
di: Du, Haikuo, et al.
Pubblicazione: (2025)

Safe Continuous-time Multi-Agent Reinforcement Learning via Epigraph Form
di: Wang, Xuefeng, et al.
Pubblicazione: (2026)

Safe Multi-agent Reinforcement Learning with Natural Language Constraints
di: Wang, Ziyan, et al.
Pubblicazione: (2024)

Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teams
di: Du, Hung, et al.
Pubblicazione: (2025)

Decentralized Collaborative Pricing and Shunting for Multiple EV Charging Stations Based on Multi-Agent Reinforcement Learning
di: Bu, Tianhao, et al.
Pubblicazione: (2024)

Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
di: Li, Tianxiao, et al.
Pubblicazione: (2026)

Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination
di: Du, Hung, et al.
Pubblicazione: (2025)

An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning
di: Amato, Christopher
Pubblicazione: (2024)

Decentralized Safe and Scalable Multi-Agent Control under Limited Actuation
di: Zinage, Vrushabh, et al.
Pubblicazione: (2024)

Reward-Independent Messaging for Decentralized Multi-Agent Reinforcement Learning
di: Yoshida, Naoto, et al.
Pubblicazione: (2025)

Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information
di: Corazza, Jan, et al.
Pubblicazione: (2025)

Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control
di: Mohaya, Turki Bin, et al.
Pubblicazione: (2026)

Algorithmic Contract Design with Reinforcement Learning Agents
di: Concha, David Molina, et al.
Pubblicazione: (2024)

Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardware
di: Smith, Keshawn, et al.
Pubblicazione: (2025)

HIPPO-MAT: Decentralized Task Allocation Using GraphSAGE and Multi-Agent Deep Reinforcement Learning
di: Ratnabala, Lavanya, et al.
Pubblicazione: (2025)

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
di: Jiang, Jiechuan, et al.
Pubblicazione: (2024)

Hybrid Agentic AI and Multi-Agent Systems in Smart Manufacturing
di: Farahani, Mojtaba A., et al.
Pubblicazione: (2025)

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
di: Dai, Pengcheng, et al.
Pubblicazione: (2025)

Resolving Conflicting Constraints in Multi-Agent Reinforcement Learning with Layered Safety
di: Choi, Jason J., et al.
Pubblicazione: (2025)

Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots
di: Mishra, Ashutosh, et al.
Pubblicazione: (2025)

A Gossip-Enhanced Communication Substrate for Agentic AI: Toward Decentralized Coordination in Large-Scale Multi-Agent Systems
di: Khan, Nafiul I., et al.
Pubblicazione: (2025)

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
di: Reyhan, Berire Gunes, et al.
Pubblicazione: (2025)

Decentralized Unlabeled Multi-Agent Navigation in Continuous Space
di: Dergachev, Stepan, et al.
Pubblicazione: (2024)

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
di: Cheruiyot, Kemboi, et al.
Pubblicazione: (2025)

Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints
di: Mguni, David, et al.
Pubblicazione: (2025)

Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
di: Geles, Ismail, et al.
Pubblicazione: (2026)

Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference
di: Wang, Hong, et al.
Pubblicazione: (2026)

An Analysis of Constraint-Based Multi-Agent Pathfinding Algorithms
di: Lee, Hannah, et al.
Pubblicazione: (2025)

Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment
di: Fan, Enguang, et al.
Pubblicazione: (2026)

Sketch2BIM: A Multi-Agent Human-AI Collaborative Pipeline to Convert Hand-Drawn Floor Plans to 3D BIM
di: Ratul, Abir Khan, et al.
Pubblicazione: (2025)

DAO-Agent: Zero Knowledge-Verified Incentives for Decentralized Multi-Agent Coordination
di: Xia, Yihan, et al.
Pubblicazione: (2025)

An Agent-Centric Dynamical Systems Perspective on Multi-Agent Reinforcement Learning
di: Rudd-Jones, James, et al.
Pubblicazione: (2025)

Semantic Fusion: Verifiable Alignment in Decentralized Multi-Agent Systems
di: Zaichyk, Sofiya
Pubblicazione: (2026)

Self-Evolving Multi-Agent Systems via Decentralized Memory
di: Hao, Guangya, et al.
Pubblicazione: (2026)

Learning Efficient Communication Protocols for Multi-Agent Reinforcement Learning
di: Zhang, Xinren, et al.
Pubblicazione: (2025)

Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning
di: Zhang, Zhiyao, et al.
Pubblicazione: (2025)

Asynchronous Credit Assignment for Multi-Agent Reinforcement Learning
di: Liang, Yongheng, et al.
Pubblicazione: (2024)

Decentralized Diffusion Policy Learning for Enhanced Exploration in Cooperative Multi-agent Reinforcement Learning
di: Zhang, Yuyang, et al.
Pubblicazione: (2026)