Instructions to execute

First, create the virtual environment and activate the environment.

virtualenv -p python3 myenv
source myenv/bin/activate

Then, we install all the required packages.

pip install -r requirements.txt

Register custom gym environment.

pip install -e gym-environments/

Now we are ready to train a DQN agent. To do this, we must execute the following command. Notice that inside the train_DQN.py there are different hyperparameters that you can configure to set the training for different topologies, to define the size of the GNN model, etc.

python train_DQN.py

Now that the training process is executing, we can see the DQN agent performance evolution by parsing the log files.

python parse.py -d ./Logs/expsample_DQN_agentLogs.txt

Finally, we can evaluate our trained model on different topologies executing the command below. Notice that in the evaluate_DQN.py script you must modify the hyperparameters of the model to match the ones from the trained model.

python evaluate_DQN.py -d ./Logs/expsample_DQN_agentLogs.txt