… To overcome this difficulty, we propose a new solution approach based on the technique
of deep reinforcement learning (DRL). Specifically, by leveraging the state-of-the-art dueling …

… Radio Environment Map (REM). For efficient acquisition, processing and utilization of the REM
data, reinforcement learning (… accurate 3D-ray-tracing radio channel model. The proposed …

… Radio map (RM) is an important tool for understanding radio environments and analyzing
network performance. It incorporates geographic information to describe the radio … of the radio …

… deep reinforcement learning. On the one hand, the deep … reinforcement learning performs
well in complex problems. The purpose of our work is that with the assistance of the radio map, …

… radio mapping. Unfortunately, the time available for detection is often limited, and in most
settings, there are no reliable models of the environment, which should be learned quickly. …

… In addition, we select the mapping function of the Q-value as φq = 1. On the other … mapping
function φa is used to map a target action to a source action. We design the action’s mapping …

… It should be noted that although reinforcement learning has been applied to the problem of …
deep reinforcement learning (DRL) algorithm and show how to apply this to the SLA mapping …

… and learning features automatically from a given data. Hence, we survey and propose a
theoretical hypothetic model formulation of deep reinforcement learning … The RL then maps the …

… To apply DRL to RRM, it is necessary to map the considered problem into an appropriate …
The goal is to find a policy that maps states to probabilities of selecting each possible action …

… A number of interesting scheduling and mapping schemes have been proposed in research
… , which inherits its main aspects from the reinforcement learning field. The model proposed …