Deep Reinforcement Learning for Optimization

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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“…Additionally, we employ the Adam (Adaptive Moment) optimization algorithm. This algorithm was first introduced by Hasan et al [154] and has been tested by researchers at OpenAI and Google Deepmind [154,155]. Adam has been found to be successful in handling non-stationary data while also being able to handle both sparse and noisy gradients.…”