Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
Chafii, Marwa; Naoumi, Salmane; Alami, Reda; Almazrouei, Ebtesam; Bennis, Mehdi; Debbah, Merouane (2023-12-18)
IEEE
18.12.2023
M. Chafii, S. Naoumi, R. Alami, E. Almazrouei, M. Bennis and M. Debbah, "Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks," in IEEE Internet of Things Magazine, vol. 6, no. 4, pp. 18-24, December 2023, doi: 10.1109/IOTM.001.2300102
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502061482
https://urn.fi/URN:NBN:fi:oulu-202502061482
Tiivistelmä
Abstract
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This article articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportuni-ties on this emerging topic.
In different wireless network scenarios, multiple network entities need to cooperate in order to achieve a common task with minimum delay and energy consumption. Future wireless networks mandate exchanging high dimensional data in dynamic and uncertain environments, therefore implementing communication control tasks becomes challenging and highly complex. Multi-agent reinforcement learning with emergent communication (EC-MARL) is a promising solution to address high dimensional continuous control problems with partially observable states in a cooperative fashion where agents build an emergent communication protocol to solve complex tasks. This article articulates the importance of EC-MARL within the context of future 6G wireless networks, which imbues autonomous decision-making capabilities into network entities to solve complex tasks such as autonomous driving, robot navigation, flying base stations network planning, and smart city applications. An overview of EC-MARL algorithms and their design criteria are provided while presenting use cases and research opportuni-ties on this emerging topic.
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