This Repository contains all the codes and relavant resources for the paper titled 'Demystifying Complex Treatment Recommendations: A Hierarchical Cooperative Multi-Agent RL Approach' published in IJCNN 2023 conference. Please cite the paper.

• Python version >= 3.5.2
• TensorFlow 1.14.0

• data : create a folder named 'data' to save the data prepared from the preprocessing script that is used for training.
• sql : data extraction sql codes using MIMIC-IV database. Run the query and save results to data dir with the same name of the queries respectively.
• preprocessing : read multiple extracted data tables from sql codes, and save processed data results to data dir
• main_discrete : Discrete action space implementation of algorithm, neural networks, setting.py containing all hyperparameters.
• main_continuous : Continuous action space implementation of algorithm, neural networks, setting.py containing all hyperparameters.
• additional_resources : contains architecture diagrams and additional experiemental results that could not be included in the paper due to space limitations.
• baseline : contains the codes of Qmix baseline for both continuos and discrete action spaces. Most other baselines used the codes shared by the authors.

Extract data from MIMIC-IV database, perform the preprocessing code preprocess_4h_mimic.py and save the processed data to data dir For all algorithms,

• cd into the HMARL_* folder, HMARL_Discrete is used for training with discrete action space and HMARL_Continuous is for continuous action space.
• Execute contextual state scripts using integrate_previous_steps.py
• Execute training script, e.g. python train_*.py -train_FM 1 -e 1, if training the state and contextual feature embedding and using them as inputs to the model. Otherwise, set -e and -train_FM to 0.
• All models of Root agent, IV-only agent, Vaso-only agent, and Qmix agent will be saved to models dir under train_* respectively.
• Training results will be saved to train_Embedding or train depending on argument -e 1 or -e 0

• Testing is automatically executed after training. Testing results will be saved to test_Embedding or test depending on argument -e 1 or -e 0