Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization

Michael Kölle, Felix Topp, Thomy Phan, Philipp Altmann, Jonas Nüßlein, Claudia Linnhoff-Popien

2024

Abstract

Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new approach to Reinforcement Learning arises using the inherent properties of quantum mechanics, reducing the trainable parameters of a model significantly. However, gradient-based Multi-Agent Quantum Reinforcement Learning methods often have to struggle with barren plateaus, holding them back from matching the performance of classical approaches. We build upon an existing approach for gradient free Quantum Reinforcement Learning and propose tree approaches with Variational Quantum Circuits for Multi-Agent Reinforcement Learning using evolutionary optimization. We evaluate our approach in the Coin Game environment and compare them to classical approaches. We showed that our Variational Quantum Circuit approaches perform significantly better compared to a neural network with a similar amount of trainable parameters. Compared to the larger neural network, our approaches archive similar results using 97.88% less parameters.

Paper Citation

in Harvard Style

Kölle M., Topp F., Phan T., Altmann P., Nüßlein J. and Linnhoff-Popien C. (2024). Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 71-82. DOI: 10.5220/0012382800003636

in Bibtex Style

@conference{icaart24,
author={Michael Kölle and Felix Topp and Thomy Phan and Philipp Altmann and Jonas Nüßlein and Claudia Linnhoff-Popien},
title={Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={71-82},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012382800003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization
SN - 978-989-758-680-4
AU - Kölle M.
AU - Topp F.
AU - Phan T.
AU - Altmann P.
AU - Nüßlein J.
AU - Linnhoff-Popien C.
PY - 2024
SP - 71
EP - 82
DO - 10.5220/0012382800003636
PB - SciTePress