This repository contains the code to replicate the experiment in the eponymous paper.
The data for the experiment (contained in Data/power_scheduling) was copied from the respository associated with Donti et al. (2017) which has an Apache 2.0 licence. We also borrow some of the data loading code in data_utils.py.
The models are built with PyTorch and cvxpylayers, implementation is in models.py. We provide a requirements.txt file to clarify other dependencies. To try the experiment with diffferent hyperparameters the hyperparameters.py file contains a function in which settings may be changed. To run the experiment call run.py in which one can define how many replications to run. The file runs the experiment function defined in experiment_whole_policy_evaluation.py which contains the whole pipeline as assembled from other files.
- Results/policy/ contains the results of the experiments that we ran including saved models.
- data_utils.py contains loading/processing functions for data and dataset classes for training and evaluation.
- evaluation_utils.py contains evaluation functions.
- optimisation_utils.py contains functions which parametrically build the cvxpy problem and optimisation layer, and the torch loss function for the DFF optimisation.