Code for Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines Paper
To create the environmnet variants we 1) fit a base model for each user and 2) impute effect sizes.
We fit 3 environment base models (reward generating function under no intervention / action): using the ROBAS 2 data set found here.
The code for fitting the environment base models is in fitting_environment_base_models.py and selecting the environment base model best fit for each ROBAS 2 user is in selecting_environment_base_models.py.
After fitting, the base model parameters and the selected base model for each ROBAS 2 user are saved to csv files in the sim_env_data folder.
Base model parameters follow the naming pattern {environment_base_model}_params.csv, and the selected base model for each user is a dictionary saved as a pickle file with the names stat_user_models.p and non_stat_user_models.p.
The code for imputing effect sizes (reward under intervention / action) and defining simulation environment variants are found in simulation_environments.py
The RL algorithm candidates are defined in rl_algorithm_candidates.py.
Experiment helper function are found in rl_experiments.py. Thre are two functions 1) run_experiment which runs the experiment without incremental recuirtment (assume all users have the same entry date) and 2) run_incremental_recruitment_exp which runs the experiment with incremental recruitment.
To run a single trial for an experiment, please call python3 run_rl_experiment.py --sim_env_type=${SIM_ENV} --algorithm_candidate=${CANDIDATE} --seed=${SEED}. (link for run_rl_experiment.py) The sim_env_type denotes the simulation environment variant name, algorithm_candidate denotes the algorithm candidates and seed denotes the number that we will seed using np.random.seed(seed_number) before beginning the trial.
Simulation Environmnet Variant Names:
["stat_het_eff_env", "non_stat_het_eff_env", "stat_pop_eff_env", "non_stat_pop_eff_env", "large_stat_het_eff_env", "large_stat_pop_eff_env", "large_non_stat_het_eff_env", "large_non_stat_pop_eff_env"]
Note: for the paper, experiments were run using the "large" effect size environment variants.
Algorithm Names:
["zip_k_1", "zip_k_4", "zip_k_full", "blr_k_1", "blr_k_4", "blr_k_full"]