Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning
Guardado en:
| 發表在: | arXiv.org (Dec 12, 2024), p. n/a |
|---|---|
| 主要作者: | |
| 其他作者: | , , |
| 出版: |
Cornell University Library, arXiv.org
|
| 主題: | |
| 在線閱讀: | Citation/Abstract Full text outside of ProQuest |
| 標簽: |
沒有標簽, 成為第一個標記此記錄!
|
| Resumen: | In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrating quantum computing principles. Quantum-Train Reinforcement Learning (QTRL) leverages parameterized quantum circuits to efficiently generate neural network parameters, achieving a \(poly(\log(N))\) reduction in the dimensionality of trainable parameters while harnessing quantum entanglement for superior data representation. The framework is designed for distributed multi-agent environments, where multiple agents, modeled as Quantum Processing Units (QPUs), operate in parallel, enabling faster convergence and enhanced scalability. Additionally, the Dist-QTRL framework can be extended to high-performance computing (HPC) environments by utilizing distributed quantum training for parameter reduction in classical neural networks, followed by inference using classical CPUs or GPUs. This hybrid quantum-HPC approach allows for further optimization in real-world applications. In this paper, we provide a mathematical formulation of the Dist-QTRL framework and explore its convergence properties, supported by empirical results demonstrating performance improvements over centric QTRL models. The results highlight the potential of quantum-enhanced RL in tackling complex, high-dimensional tasks, particularly in distributed computing settings, where our framework achieves significant speedups through parallelization without compromising model accuracy. This work paves the way for scalable, quantum-enhanced RL systems in practical applications, leveraging both quantum and classical computational resources. |
|---|---|
| ISSN: | 2331-8422 |
| Fuente: | Engineering Database |