Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning

TL;DR

The paper tackles the scalability limits of reinforcement learning by proposing Dist-QTRL, a distributed framework that uses Quantum-Train to generate policy parameters via parameterized quantum circuits. By encoding policy parameters in quantum measurement probabilities and mapping them through a classical function, Dist-QTRL achieves significant parameter reductions while enabling parallel training across multiple QPUs and potential HPC integration. The authors provide a mathematical formulation, convergence considerations, and empirical results showing competitive performance with substantial speedups and reduced model size compared to classical baselines. This work demonstrates a promising path toward scalable, quantum-enhanced RL in distributed settings, with practical implications for leveraging quantum and classical resources together in real-world tasks.

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.

Figures (1)

• Figure 1: (a) Distributed QTRL Workflow Overview: The workflow illustrates the interaction between the policy $\pi_\theta$, the environment, and the Graph Neural Network (GNN) module. Parameters are updated iteratively through the evaluation of the loss function, optimizing the mapping model $M_\theta$. The distributed training utilizes multiple agents, with distinct depths (L) for the GNN layers, to enhance learning efficiency. (b) Benchmark Comparison: This panel compares the performance of classical RL models against three centralized QTRL models with different network depths $L = [3, 7, 13]$. Results indicate that models with greater depth demonstrate better trainability, as reflected in the total reward trajectory. Notably, the distributed QTRL model with 4 agents and $L = 3$ achieves comparable performance to the centralized QTRL model with $L = 13$, showcasing the potential for distributed architectures to achieve similar efficacy with shallower networks. (c) Speedup: This plot highlights the speedup advantage of the distributed QTRL model. The baseline (dashed blue line) serves as a performance benchmark, indicating the number of episodes required by each model to reach the target reward of the centralized QTRL case. The distributed QTRL configurations with 2, 4, and 8 agents achieve the target performance significantly faster than centralized models, demonstrating a linear speedup due to the distributed approach.