Adaptive Wireless Network Management with Multi-Agent Reinforcement Learning

Ivoghlian, Ameer; Salčić, Zoran; Wang, Kevin I‐Kai

doi:10.3390/s22031019

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…There are mainly three types of approaches. (II-a) Deep reinforcement learning is a machine-learning algorithm that deals with a type of problem where a learner performs a certain state and receives a certain reward [202][203][204][205][206][207][208][209][210][211][212][213]. By repeatedly selecting actions and receiving rewards, it learns decision-making strategies for selecting actions that yield more rewards in the future.…”

Section: Interpretability Of Learning Resultsmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

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Section: Interpretability Of Learning Resultsmentioning

confidence: 99%

Applications of Deep Learning for Drug Discovery Systems with BigData

Matsuzaka

Yashiro

2022

BioMedInformatics

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“…They employed deep reinforcement learning to improve the resource allocation policy, in which the Q-network was redesigned based on the multiple replay memories to improve the training process. Ivoghlian et al [19] introduced a deep Q-network based multiagent framework for automatic network management targeting typical LoRaWAN-based IoT networks.…”

Section: A Reinforcement Learning In Iot Systemsmentioning

confidence: 99%

Edge-Enabled Two-Stage Scheduling Based on Deep Reinforcement Learning for Internet of Everything

Zhou

Liang

Yan

et al. 2023

IEEE Internet Things J.

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Internet of Everything (IoE) is playing an increasingly indispensable role in modern intelligent applications. These smart applications are known for their real-time requirements under limited network and computing resources, in which it becomes a high consuming task to transform and compute tremendous amount of raw data in cloud center. The edge-cloud computing infrastructure allows large amount of data to be processed on nearby edge nodes and then only the extracted and encrypted key features are transmitted to the data center. This offers the potential to achieve an edge-cloud based big data intelligence for IoE in a typical two-stage data processing scheme, while satisfying data security constraint. In this study, a deep reinforcement learning enhanced scheduling method is proposed to address the NP-hard challenge of two-stage scheduling, which is able to allocate computing resources within an edge-cloud infrastructure to ensure computing task to be completed with minimum cost. The proposed reinforcement learning algorithm, which incorporates the Johnson's rule, is designed to achieve an optimal schedule in IoE. The performance of our method is evaluated and compared with several existing scheduling techniques, and experiment results demonstrate the ability of our proposed algorithm in achieving a more efficient schedule with 1.1-approximation to the targeted optimal IoE applications.

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