Quantum Advantage Actor-Critic for Reinforcement Learning

Kölle, Michael; Hgog, Mohamad; Ritz, Fabian ; Altmann, Philipp ; Zorn, Maximilian ; Stein, Jonas und Linnhoff-Popien, Claudia (2024): Quantum Advantage Actor-Critic for Reinforcement Learning. ICAART 2024: International Conference on Agents and Artificial Intelligence, Rome, Italy, 24. - 26. Februar 2024. Rocha, Ana Paula; Steels, Luc und Herik, Jaap van den (eds.) : In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence, Vol. 1 Setúbal: SciTePress. pp. 297-304

Abstract

Quantum computing offers efficient encapsulation of high-dimensional states. In this work, we propose a novel quantum reinforcement learning approach that combines the Advantage Actor-Critic algorithm with variational quantum circuits by substituting parts of the classical components. This approach addresses reinforcement learning’s scalability concerns while maintaining high performance. We empirically test multiple quantum Advantage Actor-Critic configurations with the well known Cart Pole environment to evaluate our approach in control tasks with continuous state spaces. Our results indicate that the hybrid strategy of using either a quantum actor or quantum critic with classical post-processing yields a substantial performance increase compared to pure classical and pure quantum variants with similar parameter counts. They further reveal the limits of current quantum approaches due to the hardware constraints of noisy intermediate-scale quantum computers, suggesting further research to scale hybrid approaches for larger and more complex control tasks.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 978-989-758-680-4
• Place of Publication: Setúbal
• Language: English