… access management for a massive cellular IoT network to … Taking advantage of the capability
of reinforcement learning … , we design a novel reinforcement learning structure, named …

… The recent advances in deep reinforcement learning (DRL) algorithms can potentially
address the above problems of IoT systems. In this context, this paper provides a comprehensive …

… in massive IIoT, we investigate the potential machine learning solutions along with their
limitations and future research directions in the following, which is also summarized in Table 22 …

… ensuing needs of machine learning (ML)-aware frameworks in terms of resource
allocation optimization. Keeping such an issue in mind, we propose a reinforcement-learning-based, …

… potentially meet the future massive health care IoT (… reinforcement learning (RL) has proven
its strengths to provide intelligent data reliability and availability. The action-state learning …

… num- ber of IoT devices to the Internet, where the IoT devices generate massive amount of
sensory … These data could be processed and analyzed by leveraging machine learning (ML) …

… Internet of Things has introduced more openness and complexity by connecting a large …
Machine learning algorithms are also applied for IoT data analytics to discover new information, …

… of efficient algorithms for massive connectivity in IoT is also … extensive developments in
machine learning and the possibilities … research directions of incorporating machine learning (ML) …

… are presented, primarily querying the deep reinforcement learning (DRL)-based autonomy
… Federated optimization is described in this paper for maintaining massive IoT taxonomies, …

… learning algorithms such as machine learning/deep learning, … ] and [39] showed that
reinforcement learning (RL) has great … big data analytics, reinforcement learning, and deep …