Effect of Model Dissimilarity on Learning to Communicate in a Wireless Setting with Limited Information

Caryn Tran, Vignesh Subramanian, Kailas Vodrahalli and Anant Sahai

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2019-129
August 16, 2019

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-129.pdf

This work engages the problem of collaborative learning in the context of wireless communication schemes. Two agents must learn modulation and demodulation schemes that enable them to communicate with each other in the presence of an AWGN channel via reinforcement learning. Proposed and examined is the echo private preamble protocol, a communication protocol enabling two agents to learn how to communicate with little shared context. Under the echo private preamble protocol, neural network based agents to learn strategies to communicate with other neural agents as well as agents that uses a fixed standardized protocol, and agents of a different model. This work also builds iteratively on top of relaxations of this protocol to show that this information restricted protocol is comparable to ones with a larger shared context. My specific contributions lie in introducing a new model (polynomials), writing the code base, and running and analyzing the baseline experiments for the echo private preamble protocol as well as the experiments to examine the effects of learning with mismatched agents whose internal models are dissimilar.

Advisor: Anant Sahai


BibTeX citation:

@mastersthesis{Tran:EECS-2019-129,
    Author = {Tran, Caryn and Subramanian, Vignesh and Vodrahalli, Kailas and Sahai, Anant},
    Title = {Effect of Model Dissimilarity on Learning to Communicate in a Wireless Setting with Limited Information},
    School = {EECS Department, University of California, Berkeley},
    Year = {2019},
    Month = {Aug},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-129.html},
    Number = {UCB/EECS-2019-129},
    Abstract = {This work engages the problem of collaborative learning in the context of wireless communication schemes. Two agents must learn modulation and demodulation schemes that enable them to communicate with each other in the presence of an AWGN channel via reinforcement learning.
Proposed and examined is the echo private preamble protocol, a communication protocol enabling two agents to learn how to communicate with little shared context. Under the echo private preamble protocol, neural network based agents to learn strategies to communicate with other neural agents as well as agents that uses a fixed standardized protocol, and agents of a different model. This work also builds iteratively on top of relaxations of this protocol to show that this information restricted protocol is comparable to ones with a larger shared context. My specific contributions lie in introducing a new model (polynomials), writing the code base, and running and analyzing the baseline experiments for the echo private preamble protocol as well as the experiments to examine the effects of learning with mismatched agents whose internal models are dissimilar.}
}

EndNote citation:

%0 Thesis
%A Tran, Caryn
%A Subramanian, Vignesh
%A Vodrahalli, Kailas
%A Sahai, Anant
%T Effect of Model Dissimilarity on Learning to Communicate in a Wireless Setting with Limited Information
%I EECS Department, University of California, Berkeley
%D 2019
%8 August 16
%@ UCB/EECS-2019-129
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-129.html
%F Tran:EECS-2019-129