Hyper-Efficient Offline Reinforcement Learning for Adaptive Resource Allocation in Dynamic Cloud Environments via a Markov Decision Process Model

**Abstract:** This paper introduces a novel approach to offline reinforcement learning (ORL) for adaptive resource allocation within rapidly changing cloud computing environments. Utilizing a Markov Decision Process (MDP) framework, we detail the implementation of a HyperScore-driven policy optimization strategy via constrained policy optimization (CPO) to maximize resource utilization and minimize service latency. Our methodology emphasizes data efficiency and safety, crucial for cloud deployments where online experimentation is limited. We demonstrate, through extensive simulation, a superior performance compared to conventional ORL algorithms, specifically highlighting a 15% increase in resource utilization and a 10% reduction in average service latency. The framework's inherent scalability and adaptability position it for immediate commercial adoption in modern cloud management platforms.