#Installation To install the dependencies, run
python setup.py
#Training To train a model, go inside train directory and run
python Final_code.py
The training process can be monitored in sim/results/log_test (validation) and sim/results/log_central (training).
Trained model will be saved in sim/results/.
#Testing
Trained RL model needs to be copied to test/models/.
To test a trained model for the proposed solution, go inside test directory and run
python proposed.py
To test baseline 1, run
python base1.py
To test baseline 2, run
python base2.py
#Plotting Results
Results will be saved in test/results/.
To view the results, run
python plot_results.py
#Citation
@article{elgabli2018reinforcement, title={Reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks}, author={Elgabli, Anis and Khan, Hamza and Krouka, Mounssif and Bennis, Mehdi}, journal={arXiv preprint arXiv:1811.06776}, year={2018} }