Reference : Learning-based Physical Layer Communications for Multiagent Collaboration

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/42325

Title :	Learning-based Physical Layer Communications for Multiagent Collaboration
Language :	English
Author, co-author :	Mostaani, Arsham [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Simeone, Osvaldo [King's College London > Informatics]
	Chatzinotas, Symeon [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	11-Sep-2019
Main document title :	PIMRC 2019 Proceedings
Author, co-author :	Mostaani, Arsham
	Simeone, Osvaldo
	Chatzinotas, Symeon
	Ottersten, Björn
Publisher :	IEEE
Peer reviewed :	Yes
On invitation :	No
Audience :	International
City :	Istanbul
Country :	Turkey
Event name :	IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
Event date :	08-09-2019 to 11-09-2019
Event organizer :	IEEE
Event place (city) :	Istanbul
Event country :	Turkey
Keywords :	[en] Reinforcement learning ; Communication Theory ; Multi-agent systems
Abstract :	[en] Consider a collaborative task carried out by two autonomous agents that can communicate over a noisy channel. Each agent is only aware of its own state, while the accomplishment of the task depends on the value of the joint state of both agents. As an example, both agents must simultaneously reach a certain location of the environment, while only being aware of their own positions. Assuming the presence of feedback in the form of a common reward to the agents, a conventional approach would apply separately: (\emph{i}) an off-the-shelf coding and decoding scheme in order to enhance the reliability of the communication of the state of one agent to the other; and (\emph{ii}) a standard multiagent reinforcement learning strategy to learn how to act in the resulting environment. In this work, it is argued that the performance of the collaborative task can be improved if the agents learn how to jointly communicate and act. In particular, numerical results for a baseline grid world example demonstrate that the jointly learned policy carries out compression and unequal error protection by leveraging information about the action policy.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Applied Security and Information Assurance Group (APSIA)
Funders :	European Commission - EC
Name of the research project :	AGNOSTIC
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/42325
DOI :	10.1109/PIMRC.2019.8904190
Other URL :	https://ieeexplore.ieee.org/document/8904190
Framework Programme / European Project :	H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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