Skip to main content

A OpenAI Gym Env for continuous actions

Project description

gym_contin

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
  • 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_contin env =gym.make('contin-v0')

To change version

  • change version to, e.g., 1.0.7 from setup.py file
  • git clone url-here
  • cd gym-contin
  • python setup.py sdist bdist_wheel
  • twine check dist/*
  • twine upload --repository-url https://upload.pypi.org/legacy/ dist/*

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gym_contin-1.0.7.tar.gz (4.7 kB view hashes)

Uploaded Source

Built Distribution

gym_contin-1.0.7-py3-none-any.whl (5.2 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page