Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning

TL;DR

This paper presents a quantum-enhanced distributed multi-agent reinforcement learning framework that leverages quantum computing principles to improve scalability, convergence speed, and performance in high-dimensional, complex tasks.

Contribution

It introduces Dist-QTRL, a novel quantum-based framework for distributed multi-agent RL that reduces parameter dimensionality and enhances scalability using quantum entanglement.

Findings

Achieves poly(log(N)) reduction in trainable parameters.

Demonstrates faster convergence and scalability in multi-agent environments.

Shows performance improvements over classical QTRL models.

Abstract

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.