Metadata-Version: 2.1
Name: gym-discrete
Version: 1.1.7
Summary: A OpenAI Gym Env for discrete
Home-page: UNKNOWN
Author: Claudia Viaro
License: MIT
Description: # gym-discrete
        
        The domanin 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
        - sample an action (either 0 or 1)
        - if risk > 0.5 and action=1:
          - replace Xa by g, where g(\rho, Xa) is obtained using the patient's risk and Xa value
        - else:
          - do not intervene, X_a stays the same
        - give reward equal to average risk of admission, using predicted Y, initial parameters and sampled values
        
        
        # To install
        git clone url-here
        cd gym-discrete
        
        pip install gym-discrete
        import gym_discrete
        env =gym.make('discrete-v0')
        
        # To change version
        - change version to, e.g., 1.0.7 from setup.py file
        - git clone https://github.com/claudia-viaro/gym-discrete.git
        - cd gym-discrete
        - python setup.py sdist bdist_wheel
        - twine check dist/*
        - twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
