  We propose to demonstrate a network slice placement optimization solution
based on Deep Reinforcement Learning (DRL), referred to as
Heuristically-controlled DRL, which uses a heuristic to control the DRL
algorithm convergence. The solution is adapted to realistic networks with large
scale and under non-stationary traffic conditions (namely, the network load).
We demonstrate the applicability of the proposed solution and its higher and
stable performance over a non-controlled DRL-based solution. Demonstration
scenarios include full online learning with multiple volatile network slice
placement request arrivals.
