Title

Multi-robot Information Sampling Using Deep Mean Field Reinforcement Learning

Document Type

Conference Proceeding

Publication Date

1-1-2021

Subject Area

ARRAY(0x559694be11d0)

Abstract

We study the problem of information sampling of an ambient phenomenon using a group of mobile robots. Autonomous robots are being deployed for various applications such as precision agriculture, search-and-rescue, among others. These robots are usually equipped with sensors and tasked with collecting maximal information for further data processing and decision making. The studied problem is proved to be NP-Hard in the literature. To solve the stated problem approximately, we employ a multi-agent deep reinforcement learning framework and use the concepts of mean field games to potentially scale the solution to larger multi-robot systems. Simulation results show that our presented technique easily scales to 10 robots in a 19 × 19 grid environment, while consistently sampling useful information.

Publication Title

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

First Page

1215

Last Page

1220

Digital Object Identifier (DOI)

10.1109/SMC52423.2021.9658795

ISSN

1062922X

ISBN

9781665442077

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