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A OpenAI Gym Env for discrete

Project description

Gym-style API

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 all patients;
  • predict risk of admission \rho, using initialized parameters
  • sample an action (50 possible values between -2 and 2)
  • if risk > 0.2:
    • 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

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