… as a decentralised system and introduce a learning algorithm in each substrate node and …
a multiagent learning algorithm that carries out the substrate network resource management …

… and DL algorithms with an emphasis on the major aspects of the resource management in …
Tree (DT) are most commonly used supervised learning algorithms used for classification and …

… learning-based approach for wireless resource management. The key idea is to treat the input
and output of a resource allocation algorithm … (or related learning algorithms) for this type …

… ated by a single learning algorithm. Actors execute policies … LEARNING ALGORITHMIC
FRAMEWORK To provide a concrete example of how the challenges of learning RRM algorithms …

… , we design (§3) and evaluate (§4) DeepRM, a simple multi-resource cluster scheduler. …
To learn, DeepRM employs a standard policy gradient reinforcement learning algorithm [35] …

… which involved application of an ML algorithm in any of the micro … resource management
such as those belonging to natural science, material science, other business and management …

… spatial transfer learning should also consider the privacy disclosure issue. High-efficiency
learning algorithms are needed to tackle RRM challenges. Most existing DRL algorithms rely …

… to solve the resource management problems of … algorithms from the perspective of a single
resource. In addition, more and more VNE algorithms based on intelligent learning algorithms …

… As an important application of the RRM problem to showcase the capabilities of the proposed
state-augmented learning algorithm, we consider the problem of power control in m-user …

… learning (ML) has emerged as a transformative technology with the potential to optimize
resource efficiency and management … ML algorithms on enhancing resource management and …