Metadata-Version: 2.1
Name: gym_contin
Version: 1.5.0
Summary: A OpenAI Gym Env for continuous actions
Home-page: UNKNOWN
Author: Claudia Viaro
License: MIT
Description: # Gym-style API
        
        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
        - if \rho < 1/2:
          - do not intervene on X_a, which stays the same 
        - else:
          - 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-contin.git
        - cd gym-contin
        
        - !pip install gym-contin
        - import gym
        - import gym_contin
        - env =gym.make('contin-v0')
        
        # To change version
        - change version to, e.g., 1.0.7 from setup.py file
        - git clone https://github.com/claudia-viaro/gym-contin.git
        - cd gym-contin
        - 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
