A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem, Muddasar; Rizvi, Syed Tahir Hussain; Coronato, Antonio

doi:10.1109/access.2020.3038605

Cited by 142 publications

(64 citation statements)

References 295 publications

(282 reference statements)

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“…This case is regularly encountered in wireless optimization problems, which have too many local optima. [1], [12], [15], [16], [21], [23], [24], [67], [68]. Next generation wireless networks will generate a tremendous amount of data related to network statistics, such as user traffic, channel occupancy, channel quality, etc.…”

Section: Limitation Of Conventional Rram Techniquesmentioning

confidence: 99%

“…To overcome this, GANs are utilized, which generate large amounts of realistic datasets synthetically by expanding the available limited amounts of real-time datasets. From a DRL perspective, GANs-generated synthetic data is more effective and reliable than traditional augmentation methods [67]. This is because DRL agents will be exposed to various extreme challenging and practical situations by merging the realistic and synthetic data, enabling DRL models to be trained on unpredicted and rare events.…”

Section: Future Research Directionsmentioning

confidence: 99%

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Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

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<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

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Section: Limitation Of Conventional Rram Techniquesmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, the development in computing technology and the introduction of new machine learning algorithms e.g. reinforcement learning [23], neural network [21] the goal of Artificial Intelligence (AI) has become a step closer. AI has important application in diverse fields including:healthcare [12], [22], robotics and autonomous control, vision enhancing method for low vision impairments [19], natural language processing, dynamic normative environments [31], risk management [26], intelligent environments [10], games and self-organized system [11], ambient assisted living techniques to improve the quality of life of elderly [13], Social humanoid robot [9] can help to monitor indoor environmental quality [29] and distributed fuzzy system able to infer in real-time critical situations [30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Breast Cancer Using AI-Based Methods

Aamir¹,

Rahim²,

Bashir

et al. 2021

Intelligent Environments 2021

Self Cite

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Breast cancer has made its mark as the primary cause of female deaths and disability worldwide, making it a significant health problem. However, early diagnosis of breast cancer can lead to its effective treatment. The relevant diagnostic features available in the patient’s medical data may be used in an effective way to diagnose, categorize and classify breast cancer. Considering the importance of early detection of breast cancer in its effective treatment, it is important to accurately diagnose and classify breast cancer using diagnostic features present in available data. Automated techniques based on machine learning are an effective way to classify data for diagnosis. Various machine learning based automated techniques have been proposed by researches for early prediction/diagnosis of breast cancer. However, due to the inherent criticalities and the risks coupled with wrong diagnosis, there is a dire need that the accuracy of the predicted diagnosis must be improved. In this paper, we have introduced a novel supervised machine learning based approach that embodies Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network and Multilayer Perception methods. Experimental results show that the proposed framework has achieved an accuracy of 99.12%. Results obtained after the process of feature selection indicate that both preprocessing methods and feature selection increase the success of the classification system.

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“…Recently, the development in computing technology and the introduction of new machine learning algorithms e.g. reinforcement learning [22], text mining the goal of Artificial Intelligence (AI) has become a step closer. AI has important application in diverse fields including:healthcare [5]and [21], robotics and autonomous control [26] and [2],dynamic normative environments [28], ambient assisted living techniques for improvement in the quality of life of elder persons [8], drug identification [20], intelligent environments [3], games and self-organized system [4], vision enhancing method for low vision impairments [19] scheduling and management and configuration of resources, distributed fuzzy system for inferring in real-time critical situations [27], risk management [23] and computer vision.…”

Section: Introductionmentioning

confidence: 99%

Self Learning of News Category Using AI Techniques

Hayat¹,

Rahim²,

Bashir

et al. 2021

Intelligent Environments 2021

Self Cite

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Numerous e-news channels publish the daily happenings in the world from different sources. These huge amounts of news articles have lamentably conceived the information overload issue among the users. Hence text mining, which aims in extracting previously unknown information from unstructured text, has been widely used by several researchers to segregate full news articles however, the news headlines categorization is still specifically limited. Therefore, considering this limitation, the current research aims to propose a framework that will self-learn and automatically classify any given news headline into its corresponding news category using artificial intelligence methods i.e. text mining and machine learning algorithms. The proposed framework consists of three stages: Exploratory Data Analysis, Text Pre-processing, and Text Classification. For exploratory data analysis, the top 10 most frequent balanced news categories are chosen so that further processing of data can be done on a more balanced version of the dataset. After exploring the data, text pre-processing techniques are applied to make the data transformed, normalized, and structured. Finally, text classification is carried out with two approaches: unsupervised classification using Mean Shift and K-means algorithms and supervised classification using Logistic Regression with Bag of Words and TF-IDF algorithm. To depict the working of the proposed framework, a case study is presented on a news headlines dataset which accurately performed news headlines classification.

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A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Cited by 142 publications

References 295 publications

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Prediction of Breast Cancer Using AI-Based Methods

Self Learning of News Category Using AI Techniques

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