Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization

Kölle, Michael; Topp, Felix; Phan, Thomy; Altmann, Philipp ; Nüßlein, Jonas und Linnhoff-Popien, Claudia (2024): Multi-Agent Quantum Reinforcement Learning Using Evolutionary Optimization. ICAART 2024: International Conference on Agents and Artificial Intelligence, Rom, Italien, 24. - 26. Februar 2024. Rocha, Ana Paula; Steels, Luc und Herik, Jaap van den (Hrsg.): In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Bd. 1 Setúbal: SciTePress. S. 71-82

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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-989-758-680-4
• Ort: Setúbal
• Sprache: Englisch