PTDE: Personalized Training with Distilled Execution for Multi-Agent Reinforcement Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Chen, Yiqun, Mao, Hangyu, Mao, Jiaxin, Wu, Shiguang, Zhang, Tianle, Zhang, Bin, Yang, Wei, Chang, Hongxing |
|---|---|
| Formato: | Preprint |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systems
por: Zhang, Zhuohui, et al.
Publicado: (2024)
por: Zhang, Zhuohui, et al.
Publicado: (2024)
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning
por: Xu, Zhiwei, et al.
Publicado: (2024)
por: Xu, Zhiwei, et al.
Publicado: (2024)
Double Distillation Network for Multi-Agent Reinforcement Learning
por: Zhou, Yang, et al.
Publicado: (2025)
por: Zhou, Yang, et al.
Publicado: (2025)
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning
por: Amato, Christopher
Publicado: (2024)
por: Amato, Christopher
Publicado: (2024)
ToMacVF : Temporal Macro-action Value Factorization for Asynchronous Multi-Agent Reinforcement Learning
por: Zhang, Wenjing, et al.
Publicado: (2025)
por: Zhang, Wenjing, et al.
Publicado: (2025)
VIL2C: Value-of-Information Aware Low-Latency Communication for Multi-Agent Reinforcement Learning
por: Zhang, Qian, et al.
Publicado: (2025)
por: Zhang, Qian, et al.
Publicado: (2025)
IBGP: Imperfect Byzantine Generals Problem for Zero-Shot Robustness in Communicative Multi-Agent Systems
por: Mao, Yihuan, et al.
Publicado: (2024)
por: Mao, Yihuan, et al.
Publicado: (2024)
Interactive Distillation for Cooperative Multi-Agent Reinforcement Learning
por: Cho, Minwoo, et al.
Publicado: (2026)
por: Cho, Minwoo, et al.
Publicado: (2026)
Learning Efficient Communication Protocols for Multi-Agent Reinforcement Learning
por: Zhang, Xinren, et al.
Publicado: (2025)
por: Zhang, Xinren, et al.
Publicado: (2025)
GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning
por: Shi, Zifeng, et al.
Publicado: (2025)
por: Shi, Zifeng, et al.
Publicado: (2025)
Intrinsic Action Tendency Consistency for Cooperative Multi-Agent Reinforcement Learning
por: Zhang, Junkai, et al.
Publicado: (2024)
por: Zhang, Junkai, et al.
Publicado: (2024)
From Cooperation to Hierarchy: A Study of Dynamics of Hierarchy Emergence in a Multi-Agent System
por: Mao, Shanshan, et al.
Publicado: (2026)
por: Mao, Shanshan, et al.
Publicado: (2026)
Multi-Agent Reinforcement Learning for Multi-Cell Spectrum and Power Allocation
por: Zhang, Yiming, et al.
Publicado: (2023)
por: Zhang, Yiming, et al.
Publicado: (2023)
UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems
por: Chen, Yiqun, et al.
Publicado: (2026)
por: Chen, Yiqun, et al.
Publicado: (2026)
Asynchronous Credit Assignment for Multi-Agent Reinforcement Learning
por: Liang, Yongheng, et al.
Publicado: (2024)
por: Liang, Yongheng, et al.
Publicado: (2024)
Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning
por: Li, Simin, et al.
Publicado: (2025)
por: Li, Simin, et al.
Publicado: (2025)
Single-Agent Scaling Fails Multi-Agent Intelligence: Towards Foundation Models with Native Multi-Agent Intelligence
por: Hu, Shuyue, et al.
Publicado: (2025)
por: Hu, Shuyue, et al.
Publicado: (2025)
LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading
por: Lou, Chengwei, et al.
Publicado: (2025)
por: Lou, Chengwei, et al.
Publicado: (2025)
Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning
por: Lee, Min Whoo, et al.
Publicado: (2025)
por: Lee, Min Whoo, et al.
Publicado: (2025)
VissimRL: A Multi-Agent Reinforcement Learning Framework for Traffic Signal Control Based on Vissim
por: Chang, Hsiao-Chuan, et al.
Publicado: (2026)
por: Chang, Hsiao-Chuan, et al.
Publicado: (2026)
Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm
por: Li, Yihong, et al.
Publicado: (2023)
por: Li, Yihong, et al.
Publicado: (2023)
Heterogeneous Multi-Agent Task-Assignment with Uncertain Execution Times and Preferences
por: Wei, Qinshuang, et al.
Publicado: (2025)
por: Wei, Qinshuang, et al.
Publicado: (2025)
Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control
por: Mohaya, Turki Bin, et al.
Publicado: (2026)
por: Mohaya, Turki Bin, et al.
Publicado: (2026)
Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction
por: Chen, Xi, et al.
Publicado: (2025)
por: Chen, Xi, et al.
Publicado: (2025)
Attention-Guided Contrastive Role Representations for Multi-Agent Reinforcement Learning
por: Hu, Zican, et al.
Publicado: (2023)
por: Hu, Zican, et al.
Publicado: (2023)
MARPO: A Reflective Policy Optimization for Multi Agent Reinforcement Learning
por: Wu, Cuiling, et al.
Publicado: (2025)
por: Wu, Cuiling, et al.
Publicado: (2025)
Multi-Agent Reinforcement Learning with Communication-Constrained Priors
por: Yang, Guang, et al.
Publicado: (2025)
por: Yang, Guang, et al.
Publicado: (2025)
Subgoal-based Hierarchical Reinforcement Learning for Multi-Agent Collaboration
por: Xu, Cheng, et al.
Publicado: (2024)
por: Xu, Cheng, et al.
Publicado: (2024)
Field Deployment of Multi-Agent Reinforcement Learning Based Variable Speed Limit Controllers
por: Zhang, Yuhang, et al.
Publicado: (2024)
por: Zhang, Yuhang, et al.
Publicado: (2024)
Making Teams and Influencing Agents: Efficiently Coordinating Decision Trees for Interpretable Multi-Agent Reinforcement Learning
por: Chen, Rex, et al.
Publicado: (2025)
por: Chen, Rex, et al.
Publicado: (2025)
YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning
por: Zhuang, Yuan, et al.
Publicado: (2024)
por: Zhuang, Yuan, et al.
Publicado: (2024)
Graphon Mean-Field Control for Cooperative Multi-Agent Reinforcement Learning
por: Hu, Yuanquan, et al.
Publicado: (2022)
por: Hu, Yuanquan, et al.
Publicado: (2022)
Safe Continuous-time Multi-Agent Reinforcement Learning via Epigraph Form
por: Wang, Xuefeng, et al.
Publicado: (2026)
por: Wang, Xuefeng, et al.
Publicado: (2026)
Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning
por: Wang, Jing, et al.
Publicado: (2025)
por: Wang, Jing, et al.
Publicado: (2025)
LLM-ALSO: LLM-Driven Adaptive Learning-Signal Optimization for Multi-Agent Reinforcement Learning
por: Wu, Xiaoguang, et al.
Publicado: (2026)
por: Wu, Xiaoguang, et al.
Publicado: (2026)
Bottom-Up Reputation Promotes Cooperation with Multi-Agent Reinforcement Learning
por: Ren, Tianyu, et al.
Publicado: (2025)
por: Ren, Tianyu, et al.
Publicado: (2025)
Optimistic ε-Greedy Exploration for Cooperative Multi-Agent Reinforcement Learning
por: Zhang, Ruoning, et al.
Publicado: (2025)
por: Zhang, Ruoning, et al.
Publicado: (2025)
Can We Predict Before Executing Machine Learning Agents?
por: Zheng, Jingsheng, et al.
Publicado: (2026)
por: Zheng, Jingsheng, et al.
Publicado: (2026)
TVDO: Tchebycheff Value-Decomposition Optimization for Multi-Agent Reinforcement Learning
por: Hu, Xiaoliang, et al.
Publicado: (2023)
por: Hu, Xiaoliang, et al.
Publicado: (2023)
Bi-Mem: Bidirectional Construction of Hierarchical Memory for Personalized LLMs via Inductive-Reflective Agents
por: Mao, Wenyu, et al.
Publicado: (2026)
por: Mao, Wenyu, et al.
Publicado: (2026)
Ejemplares similares
-
Bridging Training and Execution via Dynamic Directed Graph-Based Communication in Cooperative Multi-Agent Systems
por: Zhang, Zhuohui, et al.
Publicado: (2024) -
Beyond Local Views: Global State Inference with Diffusion Models for Cooperative Multi-Agent Reinforcement Learning
por: Xu, Zhiwei, et al.
Publicado: (2024) -
Double Distillation Network for Multi-Agent Reinforcement Learning
por: Zhou, Yang, et al.
Publicado: (2025) -
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning
por: Amato, Christopher
Publicado: (2024) -
ToMacVF : Temporal Macro-action Value Factorization for Asynchronous Multi-Agent Reinforcement Learning
por: Zhang, Wenjing, et al.
Publicado: (2025)