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A clean implementation of imitation learning algorithms
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Imitation Learning Baseline Implementations

This project aims to provide clean implementations of imitation learning algorithms. Currently we have implementations of AIRL and GAIL, and intend to add more in the future.

To install:

sudo apt install libopenmpi-dev
conda create -n imitation python=3.7  # py3.6 is also okay.
conda activate imitation
pip install -e '.[dev]'  # install `imitation` in developer mode

To run:

# Train PPO2 agent on cartpole and collect expert demonstrations
python -m imitation.scripts.expert_demos with cartpole
# Train AIRL on from demonstrations
python -m imitation.scripts.train_adversarial with cartpole airl


  • Follow the Google Python Style Guide. Examples of Google-style docstrings can be found here.
  • Add units tests covering any new features, or bugs that are being fixed.
  • PEP8 guidelines with line width 80 and 2-space indents are enforced by ci/, which is automatically run by Travis CI.
  • Static type checking via pytype is automatically run in ci/
  • Code coverage is automatically enforced by CodeCov. The exact coverage required by CodeCov depends on the previous code coverage %. Files in imitation/{examples,scripts}/ have no coverage requirements.
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