… In this section, we first present fundamental knowledge of Markov decision … , reinforcement
learning, and deep learning techniques which are important branches of machine learning …

… capability, deep NN-based machine learning technology is … the big challenge in wireless
communications and networks … learning, unsupervised learning, and reinforcement learning, …

… challenges and open issues for future research directions. To … of reinforcement learning
methods in different wireless IoT … In this section, we provide an overview of wireless IoT …

… Therefore, in the future, to alleviate the pressure of spectrum usage in wireless communication,
based on the characteristics of DRL, this study applies DRL to wireless communication …

… reinforcement learning (RL) [5] for wireless networking. Specifically, we demonstrate that the
use of deep … affords us with two essential properties to wireless MAC: (i) fast convergence to …

… in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important
and useful technology. It is a learning … to provide an in-depth introduction of the Markov …

… communications and networking, to discuss open issues related to the application of machine
learning … share new ideas and techniques for big data analysis in communication systems. …

… Since this survey mainly focuses on deep reinforcement learning for RRAM in wireless … with
”AND/OR” combinations of them; ”deep reinforcement learning,” ”DRL,” ”resource allocation,” …

… deep reinforcement learning to power control [8]. Sun et al. [9] proposed a centralized
supervised learning approach to train a fast deep … compare the reinforcement learning outcomes …

… communication, computing, caching and control (4Cs) problems. The recent advances in deep
reinforcement learning (… is accompanied by an in-depth summary and comparison of DRL …