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Heterogeneous-Agent Reinforcement Learning Book Cover

Heterogeneous-Agent Reinforcement Learning Book Summary

by Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang
15.0 minutes

This page condenses Heterogeneous-Agent Reinforcement Learning into a quick summary with author background, historical context, and chapter takeaways so you can understand Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang's core ideas faster.

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Title
Heterogeneous-Agent Reinforcement Learning
Author
Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang
Reading Time
15.0 minutes
Category
Technology & The Future
Audio
Not available

Quick Answers

Start with the most useful search-style answers about Heterogeneous-Agent Reinforcement Learning.

Who is Yifan Zhong, Jakub Grudzien Kuba, Xidong Feng, Siyi Hu, Jiaming Ji, Yaodong Yang?

Yifan Zhong 就职于北京大学人工智能研究所和北京通用人工智能研究院；Jakub Grudzien Kuba 就职于牛津大学；Xidong Feng 就职于伦敦大学学院；Siyi Hu 就职于悉尼科技大学 ReLER, AAII；Jiaming Ji 就职于北京大学人工智能研究所；Yaodong Yang 就职...

Who should read Heterogeneous-Agent Reinforcement Learning?

对多智能体强化学习、异构智能体系统、强化学习算法设计和理论分析感兴趣的研究人员、工程师和学生。

What is the background behind Heterogeneous-Agent Reinforcement Learning?

合作多智能体强化学习（MARL）已成为人工智能研究的热点，但许多研究严重依赖智能体之间的参数共享，这限制了它们只能应用于同构智能体设置，并导致训练不稳定和缺乏收敛保证。为了解决这些挑战，本文提出了HARL算法。

Key Points

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Target Audience

对多智能体强化学习、异构智能体系统、强化学习算法设计和理论分析感兴趣的研究人员、工程师和学生。

Author Background

Yifan Zhong 就职于北京大学人工智能研究所和北京通用人工智能研究院；Jakub Grudzien Kuba 就职于牛津大学；Xidong Feng 就职于伦敦大学学院；Siyi Hu 就职于悉尼科技大学 ReLER, AAII；Jiaming Ji 就职于北京大学人工智能研究所；Yaodong Yang 就职于北京大学人工智能研究所。

Historical Context

合作多智能体强化学习（MARL）已成为人工智能研究的热点，但许多研究严重依赖智能体之间的参数共享，这限制了它们只能应用于同构智能体设置，并导致训练不稳定和缺乏收敛保证。为了解决这些挑战，本文提出了HARL算法。

Chapter Summary