Saved in:
| Main Authors: | Jin, Lyudong, Tang, Ming, Pan, Jiayu, Zhang, Meng, Wang, Hao |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.16832 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
by: He, Xingqiu, et al.
Published: (2023)
by: He, Xingqiu, et al.
Published: (2023)
MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models
by: Jin, Lyudong, et al.
Published: (2025)
by: Jin, Lyudong, et al.
Published: (2025)
EdgeTimer: Adaptive Multi-Timescale Scheduling in Mobile Edge Computing with Deep Reinforcement Learning
by: Hao, Yijun, et al.
Published: (2024)
by: Hao, Yijun, et al.
Published: (2024)
LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems
by: Duan, Tianyang, et al.
Published: (2025)
by: Duan, Tianyang, et al.
Published: (2025)
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
by: Zhang, Cui, et al.
Published: (2024)
by: Zhang, Cui, et al.
Published: (2024)
Hybrid Reinforcement Learning-based Sustainable Multi-User Computation Offloading for Mobile Edge-Quantum Computing
by: Xu, Minrui, et al.
Published: (2025)
by: Xu, Minrui, et al.
Published: (2025)
Play to Earn in the Metaverse with Mobile Edge Computing over Wireless Networks: A Deep Reinforcement Learning Approach
by: Chua, Terence Jie, et al.
Published: (2023)
by: Chua, Terence Jie, et al.
Published: (2023)
Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN
by: Kaada, Soumeya, et al.
Published: (2024)
by: Kaada, Soumeya, et al.
Published: (2024)
A Multi-Agent Reinforcement Learning Scheme for SFC Placement in Edge Computing Networks
by: Li, Congzhou, et al.
Published: (2024)
by: Li, Congzhou, et al.
Published: (2024)
Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing
by: Chen, Xiangchun, et al.
Published: (2024)
by: Chen, Xiangchun, et al.
Published: (2024)
Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks
by: Fox, Andrea, et al.
Published: (2025)
by: Fox, Andrea, et al.
Published: (2025)
DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing
by: Yamansavascilar, Baris, et al.
Published: (2021)
by: Yamansavascilar, Baris, et al.
Published: (2021)
Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computing
by: Gao, Song, et al.
Published: (2025)
by: Gao, Song, et al.
Published: (2025)
QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
by: Rahmaty, Iman, et al.
Published: (2023)
by: Rahmaty, Iman, et al.
Published: (2023)
Low-Altitude Satellite-AAV Collaborative Joint Mobile Edge Computing and Data Collection via Diffusion-based Deep Reinforcement Learning
by: Wang, Boxiong, et al.
Published: (2026)
by: Wang, Boxiong, et al.
Published: (2026)
Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
by: Wang, Wenhua, et al.
Published: (2024)
by: Wang, Wenhua, et al.
Published: (2024)
Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning
by: Chen, Chen, et al.
Published: (2025)
by: Chen, Chen, et al.
Published: (2025)
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning
by: Zhang, Yubo, et al.
Published: (2025)
by: Zhang, Yubo, et al.
Published: (2025)
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
by: Yang, Ning, et al.
Published: (2024)
by: Yang, Ning, et al.
Published: (2024)
Entropy-Aware Task Offloading in Mobile Edge Computing
by: Ardakani, Mohsen Sahraei, et al.
Published: (2026)
by: Ardakani, Mohsen Sahraei, et al.
Published: (2026)
CREWS: Collaborative Robust Edge WiFi Sensing with Asynchronous and Incomplete Observations
by: Chen, Yinan, et al.
Published: (2026)
by: Chen, Yinan, et al.
Published: (2026)
LeFi: Learn to Incentivize Federated Learning in Automotive Edge Computing
by: Zhao, Ming, et al.
Published: (2023)
by: Zhao, Ming, et al.
Published: (2023)
Adaptive Vision-Based Coverage Optimization in Mobile Wireless Sensor Networks: A Multi-Agent Deep Reinforcement Learning Approach
by: Soltani, Parham, et al.
Published: (2025)
by: Soltani, Parham, et al.
Published: (2025)
Self-Explaining Reinforcement Learning for Mobile Network Resource Allocation
by: Nowosadko, Konrad, et al.
Published: (2025)
by: Nowosadko, Konrad, et al.
Published: (2025)
Distributed Asynchronous Service Deployment in the Edge-Cloud Multi-tier Network
by: Cohen, Itamar, et al.
Published: (2023)
by: Cohen, Itamar, et al.
Published: (2023)
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing
by: Chua, Terence Jie, et al.
Published: (2023)
by: Chua, Terence Jie, et al.
Published: (2023)
Mobility-Aware Resource Allocation for mmWave IAB Networks: A Multi-Agent Reinforcement Learning Approach
by: Zhang, Bibo, et al.
Published: (2022)
by: Zhang, Bibo, et al.
Published: (2022)
Secure Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless MEC Networks
by: Hao, Xin, et al.
Published: (2023)
by: Hao, Xin, et al.
Published: (2023)
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks
by: Lozano-Cuadra, Federico, et al.
Published: (2024)
by: Lozano-Cuadra, Federico, et al.
Published: (2024)
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
by: Yu, Jiaming, et al.
Published: (2024)
by: Yu, Jiaming, et al.
Published: (2024)
Multi-AUV Cooperative Target Tracking Based on Supervised Diffusion-Aided Multi-Agent Reinforcement Learning
by: Ma, Jiaao, et al.
Published: (2026)
by: Ma, Jiaao, et al.
Published: (2026)
Applications of Multi-Agent Deep Reinforcement Learning Communication in Network Management: A Survey
by: Pi, Yue, et al.
Published: (2024)
by: Pi, Yue, et al.
Published: (2024)
FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations
by: Siew, Marie, et al.
Published: (2022)
by: Siew, Marie, et al.
Published: (2022)
Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information
by: Dongare, Sumedh J., et al.
Published: (2026)
by: Dongare, Sumedh J., et al.
Published: (2026)
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning
by: Hassan, Sheikh Salman, et al.
Published: (2024)
by: Hassan, Sheikh Salman, et al.
Published: (2024)
Collaborative Optimization of Wireless Communication and Computing Resource Allocation based on Multi-Agent Federated Weighting Deep Reinforcement Learning
by: Wu, Junjie, et al.
Published: (2024)
by: Wu, Junjie, et al.
Published: (2024)
Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks
by: Nguyen, Thai Duong, et al.
Published: (2025)
by: Nguyen, Thai Duong, et al.
Published: (2025)
AgentVNE: LLM-Augmented Graph Reinforcement Learning for Affinity-Aware Multi-Agent Placement in Edge Agentic AI
by: Zheng, Runze, et al.
Published: (2026)
by: Zheng, Runze, et al.
Published: (2026)
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks
by: Qin, Yeguang, et al.
Published: (2022)
by: Qin, Yeguang, et al.
Published: (2022)
State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordination
by: He, Qingyuan, et al.
Published: (2025)
by: He, Qingyuan, et al.
Published: (2025)
Similar Items
-
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
by: He, Xingqiu, et al.
Published: (2023) -
MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models
by: Jin, Lyudong, et al.
Published: (2025) -
EdgeTimer: Adaptive Multi-Timescale Scheduling in Mobile Edge Computing with Deep Reinforcement Learning
by: Hao, Yijun, et al.
Published: (2024) -
LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems
by: Duan, Tianyang, et al.
Published: (2025) -
Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing
by: Zhang, Cui, et al.
Published: (2024)