# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 1 Introduction  
In modern governance, municipalities serve as foundational structures for managing and addressing the needs of local populations. As the primary territorial entities in Spain, municipalities enjoy significant autonomy and authority in administrative, economic, social, and regulatory domains, enabling them to respond directly to community needs [1]. Municipal governments, organized through city councils, are tasked with governance, regulatoryfunctions, financial oversight, and the provision of essential public services, as established by the Local Government Regime [1]. However, this autonomy also presents challenges, particularly in achieving semantic interoperability across municipal administrative units--a critical requirement for efficient and coordinated public administration [2]. As public administration undergoes rapid digital transformation, municipalities increasingly rely on electronic systems for processes such as document authentication, data sharing, and regulatory compliance [1]. Nevertheless, achieving effective semantic interoperability within these frameworks remains a significant challenge. Departments like the Mayor's Office, Secretariat, and Treasury must coordinate their efforts while maintaining distinct functions and responsibilities [2]. Recent advancements in AI-based governance models utilize real-time learning abilities, enabling agents to consistently enhance their decision-making approaches in response to changing environmental conditions. The combination of MASs with DRL has been extensively examined in areas like smart city administration [3], self-driving traffic regulation [1], and financial oversight, illustrating its ability to enhance intricate, multi-party processes [4]. The JADE (Java Agent Development Framework) has become an essential instrument in promoting organized agent interactions, guaranteeing fluid communication among decentralized administrative units via standardized protocols such as FIPA-ACL. Furthermore, the integration of semantic ontologies has improved data interoperability, allowing for more coherent and efficient information sharing among different government departments [5]. Despite these advancements, challenges remain in achieving full-scale governmental interoperability, necessitating further exploration of AI-driven solutions to enhance policy implementation, automate bureaucratic workflows, and optimize public resource allocation. This study proposes developing a multi-agent system (MAS) prototype using the Java Agent Development Framework (JADE) combined with deep reinforcement learning (DRL) to enhance interoperability across municipal administrative units. Within this MAS framework, autonomous agents represent various administrative departments, enabling dynamic coordination, electronic document processing, and efficient information exchange. By integrating DRL, these agents acquire the capability to learn from their interactions, optimize decision-making processes, and adapt to the evolving demands of public administration. This adaptability fosters continuous improvements in the efficiency and responsiveness of local government services [6]. It leverages the JADE to simulate complex interoperability scenarios, providing a platform for identifying challenges and optimizing cross-departmental workflows. By enabling autonomous agents to manage tasks such as document processing, data sharing, and regulatory compliance, the MAS framework addresses critical issues in municipal governance, facilitating seamless communication and collaboration among diverse units. This integration of an MAS and DRL aims to advance the digital transformation of public administration, enhancing the coordination, adaptability, and effectiveness of municipal governance to better serve community needs and meet evolving demands [3]. The integrated system contributes to improving interoperability in local government operations.