Deep Reinforcement Learning-Based Decision Support System for Smart Environments

Abstract

The evolution of smart environments—encompassing smart cities, intelligent buildings, and automated industrial complexes—presents a fundamental challenge in systemic decision-making under uncertainty. Traditional rule-based and supervised learning approaches often fail to account for the dynamic, non-linear, and stochastic nature of these large-scale socio-technical infrastructures. This paper proposes a comprehensive systemic architecture for a Deep Reinforcement Learning (DRL)-based Decision Support System (DSS) tailored for multi-agent smart environments. We provide an extensive analytical discussion on the structural trade-offs between centralized and decentralized control, the architectural requirements for high-throughput edge-to-cloud computing, and the critical issues of algorithmic governance. The research emphasizes the socio-technical dimensions of DRL deployment, focusing on systemic robustness, environmental sustainability, and the ethical implications of automated resource allocation. By exploring the interplay between reinforcement learning agents and human-centric policy frameworks, this study argues for a paradigm shift toward "governance-aware" AI. We analyze the deployment challenges inherent in legacy infrastructures and propose a roadmap for integrating adaptive decision agents that prioritize long-term resilience and fairness. The findings suggest that while DRL offers unprecedented optimization capabilities for energy management, traffic flow, and emergency response, its successful integration requires a rigorous alignment with human institutional goals and a transparent framework for accountability in high-stakes environments.