This paper addresses a new quantum computing-based multi-agent reinforcement learning (QMARL) algorithm which is inspired by the quantum neural network (QNN)-based centralized critic and multiple actor networks. The benefit of the proposed QMARL-based algorithm is in the action control dimension reduction where it can reduce the size into a logarithmic-scale when project value measure (PVM) is utilized. Therefore, our proposed QMARL-based algorithm is beneficial for massive-agent MARL training convergence. Moreover, the various applications of QMARL-based algorithms are presented in massive-scale unmanned aerial vehicle (UAV) networks. Lastly, our performance evaluation results verify that the proposed QMARL-based algorithm can successfully converge when massive-action dimensions should be utilized.
This paper addresses a new quantum computing-based multi-agent reinforcement learning (QMARL) algorithm which is inspired by the quantum neural network (QNN)-based centralized critic and multiple actor networks. The benefit of the proposed QMARL-based algorithm is in the action control dimension reduction where it can reduce the size into a logarithmic-scale when project value measure (PVM) is utilized. Therefore, our proposed QMARL-based algorithm is beneficial for massive-agent MARL training convergence. Moreover, the various applications of QMARL-based algorithms are presented in massive-scale unmanned aerial vehicle (UAV) networks. Lastly, our performance evaluation results verify that the proposed QMARL-based algorithm can successfully converge when massive-action dimensions should be utilized.
For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multiagent reinforcement learning (MARL). However, the MARLbased algorithms suffer from huge parameter utilization and convergence difficulties with many agents. To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediatescale quantum (NISQ) era. Additionally, our QMARL is also beneficial in terms of efficient parameter utilization and fast convergence due to quantum supremacy. Note that the reward in our QMARL is defined as task precision over computation time in multiple agents, thus, multi-agent cooperation can be realized. For further improvement, an additional technique for scalability is proposed, which is called projection value measure (PVM). Based on PVM, our proposed QMARL can achieve the highest reward, by reducing the action dimension into a logarithmicscale. Finally, we can conclude that our proposed QMARL with PVM outperforms the other algorithms in terms of efficient parameter utilization, fast convergence, and scalability.
This article proposes a novel algorithm, named quantum multiagent actor–critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple UAVs, the application of multiagent reinforcement learning (MARL) techniques is regarded as a promising approach. These methods enable UAVs to learn collectively, optimizing their actions within a shared environment, ultimately leading to more efficient cooperative behavior. Furthermore, the principles of quantum computing (QC) are employed in our study to enhance the training process and inference capabilities of the UAVs involved. By leveraging the unique computational advantages of QC, our approach aims to boost the overall effectiveness of the UAV system. However, employing a QC introduces scalability challenges due to the near intermediate-scale quantum (NISQ) limitation associated with qubit usage. The proposed algorithm addresses this issue by implementing a quantum centralized critic, effectively mitigating the constraints imposed by NISQ limitations. Additionally, the advantages of the QMACN with performance improvements in terms of training speed and wireless service quality are verified via various data-intensive evaluations. Furthermore, this article validates that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access.
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-drone mobility, i.e., quantum multi-drone reinforcement learning. Our proposed framework ac-complishes 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.
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.
Advancements in quantum computer hardware and software have paved the way for the application of quantum computing across various fields. With the development of computers with thousands of qubits, the power of quantum is increasing. Because of efficiency in high action dimensions, quantum computing has advantages in multi-agent fields where the amount of data is huge. At the same time, reinforcement learning (RL) has gained prominence for its potential in unknown environments. Consequently, the integration of quantum computing and multi-agent reinforcement learning (MARL), known as quantum multi-agent reinforcement learning (QMARL), is proposed. This paper introduces the overall concept and applications of quantum computing, MARL, and QMARL. It also shows the current technology trends depending on the application. In conclusion, the paper presents challenges and future research directions for QMARL and shows that QMARL can be utilized in various applications for excellent performance.
This paper presents a comprehensive survey of Quantum Multi-Agent Reinforcement Learning (QMARL), a nascent field at the intersection of quantum computing and multi-agent systems. The survey begins by introducing the fundamentals of quantum computing, highlighting its potential to revolutionize computational capabilities. We then delve into the principles of multi-agent reinforcement learning (MARL), examining how quantum computing can enhance learning efficiency and decision-making processes in complex environments. The core of the survey focuses on the current state of QMARL, reviewing existing literature, methodologies, and case studies that demonstrate the integration of quantum algorithms with MARL frameworks. The paper also addresses the unique challenges and opportunities presented by quantum technologies in multi-agent systems, such as quantum entanglement and superposition, and their implications for agent coordination and learning dynamics. Additionally, the survey explores the practical applications of QMARL in various domains, including cybersecurity, finance, and robotics, underscoring its transformative potential. The paper concludes by identifying key research gaps and proposing future directions for the development of QMARL. This includes the need for scalable quantum algorithms, the exploration of quantum-resistant strategies in adversarial settings, and the integration of quantum principles in agent communication and collaboration. Overall, this survey serves as a foundational guide for researchers and practitioners interested in the emerging field of QMARL, offering insights into its current achievements and future possibilities.