# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 6 Conclusions  
In this article, we have developed a resource sharing scheme based on multi-agent reinforcement learning in vehicular networks. We have utilized the QMIX method to address the non-stationarity and competitive behavior of multi-agent reinforcement learning. The proposed scheme comprises two stages such as hybrid learning and distributed implementation. The QMIX showed that the average improvement of the proposed technique over MARL was 35.79% in V2I data transfer capability for varying data sizes, and the average increment in message transmission probability was 14% for all data sizes.  
The proposed deep reinforcement learning-based QMIX approach efficiently manages spectrum sharing. This adaptability helps QMIX to perform well in variable environments, thereby making it a good choice for spectrum sharing. The promising results as presented in Section 5 highlight the impact and difference of hybrid techniques. Moreover, the proposed QMIX method addresses network needs and uses the optimal way to send data to ensure smooth communication and better network performance.  
Currently, it is challenging to realize the framework due to the high computational cost practically, but as high-performance chips for AI applications mature, the proposed framework will be easy to develop.  
In future work, we plan to conduct a detailed analysis and comparison of the robustness of MARL schemes, including QMIX, and explore the combination of QMIX with other distributed methods such as the Quantile Transfer Network (QTRAN). This integration  
Figure 7: Comparison of QMIX and baseline schemes for different numbers of training episodes. (**a**) Effect of V2V data size on V2I capacity. (**b**) V2V transmission success rate.  
aims to enhance coordination and collaboration among agents in distributed systems. Additionally, we will investigate the integration of QMIX with federated learning frameworks such as Federated Q Learning and Federated Policy Gradient to enable localized decision-making and reduce communication overhead, especially in applications like UAV swarms and healthcare systems.  
Methodology, M.J., Z.U. and A.C.; Formal analysis, M.A.; Investigation, M.N.; Resources, M.A. and A.C.; Writing--original draft, M.N.; Supervision, A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data can be shared upon request. The data are not publicly available due to we are currently working on this project and exploring other dimensions of the mentioned research topic.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
**Term** & **Abbreviation** \\
**3 GPP** & 3 rd Generation Partnership Project \\
**BS** & Base Station \\
**CSI** & Channel State Information \\
**D2D** & Device-to-Device \\
**DNN** & Deep Neural Network \\
**DQN** & Deep Q-Network \\
**DRL** & Deep Reinforcement Learning \\
**ITS** & Intelligent Transportation Systems \\
**LTE** & Long-Term Evolution \\
**MA-MDP** & Multi-Agent Markov Decision Process \\
**MARL** & Multi-Agent Reinforcement Learning \\
**MDP** & Markov Decision Process \\
**QTRAN** & Quantile Transfer Network \\
**RAS** & Robotics and Autonomous Systems \\
**RB** & Resource Block \\
**RL** & Reinforcement Learning \\
**RSU** & Roadside Unit \\
**SINR** & Signal-to-Interference-plus-Noise Ratio \\
**TD** & Temporal Difference \\
**VCN** & Vehicular Communication Networks \\
**VN** & Vehicular Networks \\
**V2I** & Vehicle-to-Infrastructure \\
**V2V** & Vehicle-to-Vehicle) \\
**V2X** & Vehicle-to-Everything communication \\
**WMMSE** & Weighted Minimum Mean Square Error \\ \end{tabular}