Strategy-planned Q-learning Approach for Multi-robot Task Allocation

H. Hilal Ezercan Kayir, Osman Parlaktuna

2014

Abstract

In market-based task allocation mechanism, a robot bids for the announced task if it has the ability to perform the task and is not busy with another task. Sometimes a high-priority task may not be performed because all the robots are occupied with low-priority tasks. If the robots have an expectation about future task sequence based-on their past experiences, they may not bid for the low-priority tasks and wait for the high-priority tasks. In this study, a Q-learning-based approach is proposed to estimate the time-interval between high-priority tasks in a multi-robot multi-type task allocation problem. Depending on this estimate, robots decide to bid for a low-priority task or wait for a high-priority task. Application of traditional Q-learning for multi-robot systems is problematic due to non-stationary nature of working environment. In this paper, a new approach, Strategy-Planned Distributed Q-Learning algorithm which combines the advantages of centralized and distributed Q-learning approaches in literature is proposed. The effectiveness of the proposed algorithm is demonstrated by simulations on task allocation problem in a heterogeneous multi-robot system.

Paper Citation

in Harvard Style

Hilal Ezercan Kayir H. and Parlaktuna O. (2014). Strategy-planned Q-learning Approach for Multi-robot Task Allocation . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-040-6, pages 410-416. DOI: 10.5220/0005052504100416

in Bibtex Style

@conference{icinco14,
author={H. Hilal Ezercan Kayir and Osman Parlaktuna},
title={Strategy-planned Q-learning Approach for Multi-robot Task Allocation},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2014},
pages={410-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005052504100416},
isbn={978-989-758-040-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Strategy-planned Q-learning Approach for Multi-robot Task Allocation
SN - 978-989-758-040-6
AU - Hilal Ezercan Kayir H.
AU - Parlaktuna O.
PY - 2014
SP - 410
EP - 416
DO - 10.5220/0005052504100416