… agent to learn an accurate representation of the system. Then, when it comes to closedloop
control through Deep Reinforcement Learning (… of learning a table of Q-values, we learn the …

… While beginners can learn about the novel techniques to … mixed numerology, and Part II is
focused on layers higher than PHY. … Then a multi-agent reinforcement learning based smart …

… the SCS numerology … agent can be misleading when the information and reward are not
accurate [27]. To accurately judge an agent’s selected action, we adopt an intermittent learning …

… The principle is that timefrequency resources with the same numerology are grouped together
… puts the traffic mix and the intent requirements to be met by the slices. Table II provides an …

… We define the DRP design by: State: The DRP agent considers … The base station uses
numerology 1 and a TDD period of 5ms. … The Mixed slot is required between UL and DL slots to …

… We first model it as a multi-agent reinforcement learning (RL) system with compound action
… as a mixed-integer optimization problem. The transformation and variable substitution have …

… The first one is mixing HARQ Ids of different HARQ processes, and the second is missing
re… The agent was trained using Deep QLearning to optimally select UEs for GF access, and …

… of machine learning (ML) and Decision Tree (DT) learning. … from the original dataset with
mixed class labels. The training … We demonstrated that adapting the numerology on the PHY …

… Giacomo, “A reinforcement learning agent for mixednumerology interference-aware slice
spectrum allocation with nondeterministic and deterministic traffic,” Comput. Commun., vol. 189…

… In order to learn the policy online, we propose a novel kernel-… are exploited to make policy
learning more adaptive. Numerical … the accumulated gradients at the virtual agent k ∈ {1,...,K}, …