Reinforcement Learning for Dynamic Resource Allocation in Intelligent Systems

Javier Berrocal

Reinforcement Learning for Dynamic Resource Allocation in Intelligent Systems

Author(s)	Dr. Javier Berrocal
Country	United States
Abstract	Dynamic resource allocation is a fundamental challenge in intelligent computing systems in which computational demand, communication traffic, energy availability, queue conditions, and service priorities vary continuously. Conventional static allocation policies are simple to implement but may perform poorly when operating conditions change rapidly, while rule-based adaptive methods can become difficult to tune across heterogeneous environments. Reinforcement learning offers an alternative by allowing an agent to improve sequential allocation decisions through interaction with an environment and feedback from a reward function. This study examines the suitability of reinforcement learning for dynamic resource allocation through a conceptual review and a reproducible synthetic queueing simulation. The experimental environment models changing workload demand, finite resource capacity, queue accumulation, allocation cost, and service-level constraints. A tabular Q-learning policy is compared with static allocation and an adaptive heuristic under four levels of demand volatility. The simulation uses a fixed random seed, explicit state discretization, five resource-allocation actions, 1,800 training episodes, and a 12,000-step evaluation horizon for each condition. Results show that differences between policies become substantially more pronounced as workload volatility increases. At the highest simulated volatility, Q-learning maintains a service-level satisfaction rate of 94.46%, compared with 62.68% for static allocation and 60.58% for the adaptive heuristic. The findings demonstrate the potential of reinforcement learning to maintain adaptive performance under nonstationary resource demand while also highlighting the importance of reward design, safe exploration, reproducibility, scalability, and deployment-specific validation. The paper concludes that reinforcement learning is most valuable when resource management is sequential, feedback-driven, and sufficiently dynamic to justify adaptive policy learning.
Keywords	reinforcement learning, dynamic resource allocation, intelligent systems, Q-learning, adaptive computing, resource scheduling, workload management, service-level optimization
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2019
Published On	2019-04-30

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Reinforcement Learning for Dynamic Resource Allocation in Intelligent Systems

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