#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} }