Michal Butkiewicz, Sunny Soni, Andrzej Szczepura

For anaconda, use the command : conda env create -f environment.yml in the main directory.

For vanilla python virtual environment or installation, you can also use : pip install -r requirements.txt

Agent_Final.py : contains our self designed gym environment preprocess.py : contains code for preprocessing the xlsx files for our environment DDQN_Agent.py : contains the DDQN based RL agent ddqn_train.py : contains code for training and validating our DDQN agent on our custom environment tabular_qlearning.py : contains the Tabular Qlearning based RL agent as well as code for training and testing the models random_baseline.py : code for testing our agent with random actions on validation set

exploratory_data_analysis.ipynb : EDA python notebooks Plots.ipynb : Python notebooks with the code used for plotting graphs for our report ddqn_plot_v_values.py : code for plotting the v-value plots

In all the experiment files, you can change the mode variable to 'train' to train your own models using the custom agent.

1. Use ddqn_train.py for training/validation.
2. Look for parameters to configure using the class definition in DDQN_Agent.py.

1. Use tabular_qlearning.py for training and validation.

1. Use random_baseline.py for console output.

main.py makes use of our pretrained ddqn agent using the basic set to work on the validation set provided in validate.xlsx. The model used for this is provided in the same directory and should work out of the box.

If model and data directory do not exist, the validation will fail to work. Train your models first in these cases before trying to validate the custom environment.