التفاصيل البيبلوغرافية
| العنوان: |
Learning Task-Oriented Channel Allocation for Multi-Agent Communication. |
| المؤلفون: |
He, Guojun1 guojunhe@hust.edu.cn, Cui, Shibo2 shibocui@hust.edu.cn, Dai, Yueyue2 yueyuedai@ieee.org, Jiang, Tao2 taojiang@hust.edu.cn |
| المصدر: |
IEEE Transactions on Vehicular Technology. Nov2022, Vol. 71 Issue 11, p12016-12029. 14p. |
| مصطلحات موضوعية: |
*ARTIFICIAL intelligence, REINFORCEMENT learning, PARTIALLY observable Markov decision processes |
| مستخلص: |
Benefiting from the rapid progress of wireless communication and artificial intelligence, multi-agent collaboration opens up new opportunities for various fields. To facilitate multi-agent acting as a group, effective communication plays a crucial role. Recently, many efforts based on multi-agent reinforcement learning have been made to enable effective multi-agent communication under limited bandwidth or noisy channel. However, current methods do not explore wireless resource allocation strategy explicitly. Moreover, due to ignoring task-relevant significance of information, traditional wireless resource allocation schemes may fail to guarantee the transmission efficiency and reliability for multi-agent communication. To this end, in this paper, we propose a task-oriented communication principle for multi-agent communication. We model the task-oriented channel allocation problem as a decentralized partially observable Markov decision process and propose a multi-agent reinforcement learning framework as a solution. Specifically, we design a novel variational information bottleneck to extract task-relevant information from local observation. Furthermore, a task-oriented channel allocation mechanism is developed to choose the allocation pattern with maximum expected gain. Finally, a double attention mechanism is developed to motivate the efficient utilization of task-relevant information. Experimental results show that our method can improve the effectiveness and efficiency of multi-agent communication, enhancing collaboration performance compared to baselines. [ABSTRACT FROM AUTHOR] |
|
Copyright of IEEE Transactions on Vehicular Technology is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |
