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

… reinforcement learning (RL). We discuss … of machine learning, deep learning and reinforcement
learning. Next we discuss core RL elements, including value function, in particular, Deep …

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

… 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 …

… 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 …

… This section presents a holistic overview of the potential research directions and the …
forward by the use of ML algorithms in the MAC protocol design for future wireless networks. …

… Extensive motivation is given for why deep learning based on … and operation of future
wireless communication networks, and … , such as reinforcement learning and transfer learning. A …

… deep-learning-assisted optimization for resource allocation. We then highlight the deep
reinforcement learning … rich expert knowledge in wireless communications developed over the …