张恒溪|ZHANG Hengxi

张恒溪|ZHANG Hengxi

Tsinghua University

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Posts

Future Blog Post

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Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Published in , 1900

Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net

Published in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022

In this work, we represent a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity.

Recommended citation: Hengxi Zhang, Huaze Tang, Yuanquan Hu, Xiaoli Wei, Chenye Wu, Wenbo Ding, and Xiao-Ping Zhang. "Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net." In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), pp. 1-5. IEEE, 2022. https://ieeexplore.ieee.org/abstract/document/10012942

Published in , 1900

Mean-Field-Aided Multiagent Reinforcement Learning for Resource Allocation in Vehicular Networks

Published in IEEE Internet of Things Journal, 2023

In this article, we propose a novel method MF-MARL that combines mean-field game with MARL to decrease the computational complexity in a decentralized multi-agent system while maintaining a near-optimal performance.

Recommended citation: Hengxi Zhang, Chengyue Lu, Huaze Tang, Xiaoli Wei, Le Liang, Ling Cheng, Wenbo Ding, and Zhu Han. " Mean-Field-Aided Multiagent Reinforcement Learning for Resource Allocation in Vehicular Networks." IEEE Internet of Things Journal 10, no. 3 (2022): 2667-2679. https://ieeexplore.ieee.org/abstract/document/9919273

Graphon Mean-Field Control for Cooperative Multi-Agent Reinforcement Learning

Published in Elsevier Journal of the Franklin Institute, 2023

In this paper, we introduce a graphon mean-field control framework that introduces graphon theory to the mean-field paradigm to approximate cooperative heterogeneous multi-agent reinforcement learning with nonuniform interactions and heterogeneous reward functions and state transition functions among agents.

Recommended citation: Yuanquan Hu, Xiaoli Wei, Junji Yan, Hengxi Zhang, Graphon Mean-Field Control for Cooperative Multi-Agent Reinforcement Learning, Journal of the Franklin Institute, Sept. 1, 2023 https://www.sciencedirect.com/science/article/pii/S0016003223005483

Published in , 1900

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