Install environment
Open trainagentpar.py and change parameters and hyperparameters.
You can try only change some parameters in line 92 and 155
patient_name='adult#010', #Patient name: adult#001-010, adolescent#001-010 and child#001-010
reward_fun=stepReward3, #Change to your reward function
normalize=True, #Normalize input
sequence=15, #Insulin injection interval, 1 seq = 3 mins
harrison_benedict=True)), #Use Harrison-Benedict's meal schedule
370 #Step time limit
Command for training:
python trainagentC1.py --root_dir log/patient --alsologtostderr > log.txt 2> logerror.txt
For evaluation in evalagent.py also same as above. You can use trained policies in ppo-rnn-policies folder. Command for evaluation:
python evalagent.py --root_dir logeval/patient --saved_dir ppo-rnn-policies/XXX/policy_saved_model/policy_000006400/ > log.txt 2> logerror.txt
You can set how many random seeds and days to run in BBGreedy.py and PIDGreedy.py