Deep Reinforcement Learning for Optimization

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Author(s): Md Mahmudul Hasan (Anglia Ruskin University, UK), Md Shahinur Rahman (Daffodil International University, Bangladesh)and Adrian Bell (Anglia Ruskin University, UK)
Copyright: 2021
Pages: 17
Source title: Research Anthology on Artificial Intelligence Applications in Security
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-7705-9.ch070

Keywords: Artificial Intelligence / Computer Science & IT / Cyber and Network Security / Information Science Reference

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Abstract

Deep reinforcement learning (DRL) has transformed the field of artificial intelligence (AI) especially after the success of Google DeepMind. This branch of machine learning epitomizes a step toward building autonomous systems by understanding of the visual world. Deep reinforcement learning (RL) is currently applied to different sorts of problems that were previously obstinate. In this chapter, at first, the authors started with an introduction of the general field of RL and Markov decision process (MDP). Then, they clarified the common DRL framework and the necessary components RL settings. Moreover, they analyzed the stochastic gradient descent (SGD)-based optimizers such as ADAM and a non-specific multi-policy selection mechanism in a multi-objective Markov decision process. In this chapter, the authors also included the comparison for different Deep Q networks. In conclusion, they describe several challenges and trends in research within the deep reinforcement learning field.

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Deep Reinforcement Learning for Optimization

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