ASN: action semantics network for multiagent reinforcement learning

In multiagent systems (MASs), each agent makes individual decisions but all contribute globally to the system’s evolution. Learning in MASs is difficult since each agent’s selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and...

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Bibliographic Details
Published in:	Autonomous agents and multi-agent systems Vol. 37; no. 2
Main Authors:	Yang, Tianpei, Wang, Weixun, Hao, Jianye, Taylor, Matthew E., Liu, Yong, Hao, Xiaotian, Hu, Yujing, Chen, Yingfeng, Fan, Changjie, Ren, Chunxu, Huang, Ye, Zhu, Jiangcheng, Gao, Yang
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-12-2023 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computer & video games Computer Science Computer Systems Organization and Communication Networks Coordination Deep learning Machine learning Multiagent systems Neural networks Performance enhancement Semantics Software Engineering/Programming and Operating Systems User Interfaces and Human Computer Interaction Multiagent coordination Deep reinforcement learning Multiagent reinforcement learning
Online Access:	Get full text
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