Distributing data throughout a MANET in a communications denied environment: reinforcement learning and game theory approaches

Dan Zwillinger; Jen Sierchio; Mark Gerken; Emily Clark

doi:10.1117/12.2618517

6 June 2022 Distributing data throughout a MANET in a communications denied environment: reinforcement learning and game theory approaches

Dan Zwillinger, Jen Sierchio, Mark Gerken, Emily Clark

Author Affiliations +

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130G (2022) https://doi.org/10.1117/12.2618517
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

This paper determines optimal strategies for transmitting messages in a mobile ad-hoc network (MANET) in a communications-limited and lossy communications environment. When an agent generates or receives a message it must decide to which neighbors, and how many times, that message is to be passed. The opposing goals are to (1) propagate all messages throughout the MANET quickly and (2) to minimize the total number of messages sent. We compare two optimized decision strategies for the agents, reinforcement learning (RL) and game theory (GT) methods. For the RL framework, each node in the MANET acts as a reinforcement learning agent who must learn optimal decisions for when to send messages to whom. For the GT framework, we create a game tree where the nodes encompass message knowledge and connectivity information, and the decision branches represent sending messages to neighbors. We solve the game using a Monte-Carlo Tree Search (MCTS) variation to determine the probability that a message is sent to a neighbor. Performance is assessed in terms of the total number of messages sent, and the length of time for a given percentage of messages to reach a given percentage of nodes. Experiments with MANETs of varying size and connectivity are considered, and the RL and GT performance and training speed are compared. The decision strategies are domain agnostic and may be applied to ground, air, surface, sub-surface, or satellite networks.

Conference Presentation

Citation Download Citation

Dan Zwillinger, Jen Sierchio, Mark Gerken, and Emily Clark "Distributing data throughout a MANET in a communications denied environment: reinforcement learning and game theory approaches", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130G (6 June 2022); https://doi.org/10.1117/12.2618517

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available