 |
Among AI techniques, Reinforcement Learning (RL), due
to its operational characteristics and its continuous interactions
with its environment to learn via trial-and-error, can be a viable
option in network control and management, especially due to
its ability to respond to a changing environment
Comparing this to Supervised and Unsupervised Learning the algorithm must be fully trained and then applied to a
dataset for testing. If the algorithm is not able to respond as
predicted, it would require further re-training of the algorithm,
which is not scalable for networking due to the continuous
learning required. Such techniques can also be used for
translating intents into network policies, which can be done
in conjunction with AI models to monitor performance and
detect network problems
|
IEEE/ICACT20210131 Slide.06
[Big Slide]