… 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}, …

… In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty
… networks based on multi-agent reinforcement learning,” IEEE J. Sel. Areas Commun., vol. …

… hybrid beamforming architecture with a mixed analog-to-digital … NUMEROLOGY FOR HST
Numerology optimization for HST … a deep reinforcement learning multi-agent power allocation …

… mixed Neoplatonism and Kabbalah for the first time. The books covered diverse topics such
as the elements, numerology, … We can reject the bigotry and still learn from great occultists of …

… use of deep learning (DL), deep reinforcement learning (DRL… eMBB and URLLC services
to best accommodate the mixed … structure such as frequency numerology and service period. …

… learning (DRL), an important branch of machine learning belonging to AI, can be adopted to
train an agent to … and random scheduling for both overall mixed and individual traffic types, …