A distributed joint-learning and auction algorithm for target assignment

Teymur Sadikhov, Minghui Zhu, and Sonia Martínez
Proceedings of the IEEE Int. Conference on Decision and Control, Atlanta, GA, December 2010

Abstract:

We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents do not know this graph or the locations of the targets on it. However, they can obtain local information about these by local sensing and communicating with other agents within a limited range. To solve this problem, we come up with a new distributed algorithm that integrates Q-Learning and a distributed auctions. The Q-Learning part helps estimate the assignment benefits for each agent-target pair, while the auction part takes care of assigning agents to targets in a distributed and almost optimal fashion. The algorithms are shown to terminate with a near-optimal assignment in a finite time.

File: main.pdf (Extended version)

Bib-tex entry:

@InProceedings{TS-MZ-SM:10-cdc,
author = {T. Sadikhov and M. Zhu and S. Mart{\'\i}nez},
booktitle = {IEEE International Conference on Decision and Control},
title = {A distributed joint-learning and auction algorithm for target assignment},
year = {2010},
month = {December},
address = {Atlanta, USA},
pages = {5450--5455}
}