Effective deep Q-networks (EDQN) strategy for resource allocation based on optimized reinforcement learning algorithm

The healthcare industry has always been an early adopter of new technology and a big benefactor of it. The use of reinforcement learning in the healthcare system has repeatedly resulted in improved outcomes.. Many challenges exist concerning the architecture of the RL method, measurement metrics, and model choice. More significantly, the validation of RL in authentic clinical settings needs further work. This paper presents a new Effective Resource Allocation Strategy (ERAS) for the Fog environment, which is suitable for Healthcare applications. ERAS tries to achieve effective resource management in the Fog environment via real-time resource allocating as well as prediction algorithms. Comparing the ERAS with the state-of-the-art algorithms, ERAS achieved the minimum Makespan as compared to previous resource allocation algorithms, while maximizing the Average Resource Utilization (ARU) and the Load Balancing Level (LBL). For each application, we further compared and contrasted the architecture of the RL models and the assessment metrics. In critical care, RL has tremendous potential to enhance decision-making. This paper presents two main contributions, (i) Optimization of the RL hyperparameters using PSO, and (ii) Using the optimized RL for the resource allocation and load balancing in the fog environment. Because of its exploitation, exploration, and capacity to get rid of local minima, the PSO has a significant significance when compared to other optimization methodologies.

Automated summary

The healthcare industry has seen significant benefits from the adoption of new technology, particularly in the use of reinforcement learning. This paper presents a resource allocation strategy for the healthcare industry using fog computing and optimized reinforcement learning models. The study highlights the potential of reinforcement learning in improving decision-making in critical care settings.