Reference : Learning-based Physical Layer Communications for Multiagent Collaboration
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Computational Sciences
http://hdl.handle.net/10993/42325
Learning-based Physical Layer Communications for Multiagent Collaboration
English
Mostaani, Arsham mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Simeone, Osvaldo mailto [King's College London > Informatics]
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
11-Sep-2019
PIMRC 2019 Proceedings
Mostaani, Arsham mailto
Simeone, Osvaldo mailto
Chatzinotas, Symeon mailto
Ottersten, Björn mailto
IEEE
Yes
No
International
Istanbul
Turkey
IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
08-09-2019 to 11-09-2019
IEEE
Istanbul
Turkey
[en] Reinforcement learning ; Communication Theory ; Multi-agent systems
[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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Applied Security and Information Assurance Group (APSIA)
European Commission - EC
AGNOSTIC
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/42325
10.1109/PIMRC.2019.8904190
https://ieeexplore.ieee.org/document/8904190
H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
Learning-based Physical Layer Communications for Multiagent Collaboration.pdfAuthor preprint864.46 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.