Y.3181 is an ITU-T Recommendation specifying an Architectural framework for Machine Learning Sandbox in future networks (e.g. 5G, IMT-2020).

[1] The standard describes the requirements and architecture for a machine learning sandbox (computer security) a in future networks including IMT-2020.

[2] However, such datasets may be limited and/or too complex, thus questions arise regarding the accuracy of the output of the ML mechanism.

In particular, reducing the generalization error is the main concern in applying any kind of Supervised Learning (SL) approach, which can be high even if the test error is kept low (this phenomenon is commonly known as overfitting).

RL has been shown to be of great utility for single-agent approaches in controlled scenarios, however notable adverse effects can appear as a result of the competition raised by multiple systems sharing the same resources (e.g., while providing heterogeneous services using common network resources).