Reinforcement Learning Models for Optimal Resource Allocation in Smart Enterprises

Abstract

This study explores the application of reinforcement learning (RL) models for optimal resource allocation in smart enterprises, where dynamic, data-driven environments require adaptive and autonomous decision-making. By modeling enterprise resource allocation as a sequential decision process, RL agents learn optimal policies through continuous interaction with operational systems, balancing competing objectives such as cost efficiency, productivity, energy consumption, and service quality. The proposed framework integrates real-time enterprise data, predictive analytics, and feedback-driven learning to enable intelligent allocation of financial, human, and computational resources under uncertainty. Experimental evaluations and simulated enterprise scenarios demonstrate that RL-based approaches outperform traditional rule-based and optimization methods by improving utilization efficiency, responsiveness to demand fluctuations, and long-term strategic performance. The findings highlight the potential of reinforcement learning as a core enabler of self-optimizing, resilient, and sustainable smart enterprises.