Optimising radiation therapy treatment schedules with deep reinforcement learning

• The model folder contains the code of the Python implementation of the simulation.

• The model_cpp folder contains the code of the C++ implementation of the simulation.

• The nnets folder contains the neural networks trained with different algorithms and reward functions as explained in the manuscript.

• The training_logs folder contains the training log files of the four agents described in the manuscript inside zip archives.

• The eval folder contains the performance evaluations of the different agents.

• The tmp folder contains the images created during the evaluation of the agents.

• The misc folder contains files that couldn't be classified in the preceding folders.

• main.py is used to train an agent.

• use_network.py evaluates a neural network using the performance indicators described in the manuscript.