Factorization Q-learning Initialization

The proposed framework works as an add-on of Q-learning and captures the potential correlation between states and actions from the explored experiences to predict the unknown Q-values in an open-source simulated 5G network.

How to use

• Set the number of antennas in the base station. In environment.py change the line self.M_ULA to the values of your choice. The code expects M = 4, 8, 16, 32, and 64.
• Run Q-learning and its variants algorithms. Run the scripts main_QL.py, main_DynaQ.py, main_DQL.py,main_QlL.py, and main_SQL.py. The result is the same as that in folder Results.
• Show the results. Run the script Results_plot.ipynb in folder Results to show the figures and tables in the paper.