A OpenAI Gym Env for continuous control
Project description
model_update
The domain features a continuos state and a dicrete action space.
The environment initializes:
cross-sectional dataset with variables X_a, X_s, Y and N observations; logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2; The agent:
sees a patient (sample observation); predict his risk of admission \rho, using initialized parameters intervene on X_a sample an action a in [0,1] compute g(a, X_a) = newX_a intervene on X_a by updating it to newX_a give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values (shouldn't I fit a new logit-link? parameters are now diff?)
To install
- git clone https://github.com/claudia-viaro/gym-update.git
- cd gym-update
- !pip install gym-update
- import gym
- import gym_update
- env =gym.make('update-v0')
To change version
- change version to, e.g., 1.0.7 from setup.py file
- git clone https://github.com/claudia-viaro/gym-update.git
- cd gym-update
- python setup.py sdist bdist_wheel
- twine check dist/*
- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
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