International Conference on Information and Communication Technology Convergence (ICTC)
Abstract
As network architecture becomes complex and the
user requirement gets diverse, the role of efficient network resource management becomes more important. However, existing
network scheduling algorithms such as the max-weight algorithm
suffer from poor delay performance. In this paper, we present
a reinforcement learning-based network scheduling algorithm
that achieves both optimal throughput and low delay. To this
end, we first formulate the network optimization problem as
an MDP problem. Then we introduce a new state-action value
function called W-function and develop a reinforcement learning
algorithm called W-learning that guarantees little performance
loss during a learning process. Finally, via simulation, we verify
that our algorithm shows delay reduction of up to 40.8%
compared to the max-weight algorithm over various scenarios.