Reinforcement Learning in the Load Balancing Problem for the iFDAQ of the COMPASS Experiment at CERN

Ondřej Šubrt, Martin Bodlák, Matouš Jandek, Vladimír Jarý, Antonín Květoň, Josef Nový, Miroslav Virius, Martin Zemko

2020

Abstract

Currently, modern experiments in high energy physics impose great demands on the reliability, efficiency, and data rate of Data Acquisition Systems (DAQ). The paper deals with the Load Balancing (LB) problem of the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN and presents a methodology applied in finding optimal solution. Machine learning approaches, seen as a subfield of artificial intelligence, have become crucial for many well-known optimization problems in recent years. Therefore, algorithms based on machine learning are worth investigating with respect to the LB problem. Reinforcement learning (RL) represents a machine learning search technique using an agent interacting with an environment so as to maximize certain notion of cumulative reward. In terms of RL, the LB problem is considered as a multi-stage decision making problem. Thus, the RL proposal consists of a learning algorithm using an adaptive ε–greedy strategy and a policy retrieval algorithm building a comprehensive search framework. Finally, the performance of the proposed RL approach is examined on two LB test cases and compared with other LB solution methods.

Paper Citation

in Harvard Style

Šubrt O., Bodlák M., Jandek M., Jarý V., Květoň A., Nový J., Virius M. and Zemko M. (2020). Reinforcement Learning in the Load Balancing Problem for the iFDAQ of the COMPASS Experiment at CERN. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 734-741. DOI: 10.5220/0009035107340741

in Bibtex Style

@conference{icaart20,
author={Ondřej Šubrt and Martin Bodlák and Matouš Jandek and Vladimír Jarý and Antonín Květoň and Josef Nový and Miroslav Virius and Martin Zemko},
title={Reinforcement Learning in the Load Balancing Problem for the iFDAQ of the COMPASS Experiment at CERN},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={734-741},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009035107340741},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Reinforcement Learning in the Load Balancing Problem for the iFDAQ of the COMPASS Experiment at CERN
SN - 978-989-758-395-7
AU - Šubrt O.
AU - Bodlák M.
AU - Jandek M.
AU - Jarý V.
AU - Květoň A.
AU - Nový J.
AU - Virius M.
AU - Zemko M.
PY - 2020
SP - 734
EP - 741
DO - 10.5220/0009035107340741