Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning

TL;DR

This paper introduces a simulation software framework for quantum multi-drone reinforcement learning, demonstrating stable training, efficient reward convergence, and enhanced analysis capabilities for quantum multi-agent systems.

Contribution

It presents a novel software framework for quantum multi-agent reinforcement learning controlling multi-drones, with improved stability and analysis features over classical approaches.

Findings

Achieves stable training results and reasonable reward convergence.

Uses fewer trainable parameters for effective performance.

Provides tools for detailed analysis of training processes.

Abstract

Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) features non-stationarity and uncertain properties. Therefore, this paper presents a simulation software framework for novel QMARL to control autonomous multi-drones, i.e., quantum multi-drone reinforcement learning. Our proposed framework accomplishes reasonable reward convergence and service quality performance with fewer trainable parameters. Furthermore, it shows more stable training results. Lastly, our proposed software allows us to analyze the training process and results.