Metadata-Version: 2.1
Name: imitation
Version: 0.1.0
Summary: Implementation of modern IRL and imitation learning algorithms.
Home-page: https://github.com/HumanCompatibleAI/imitation
Author: Center for Human-Compatible AI and Google
License: MIT
Description: [![CircleCI](https://circleci.com/gh/HumanCompatibleAI/imitation.svg?style=svg)](https://circleci.com/gh/HumanCompatibleAI/imitation)
        [![Documentation Status](https://readthedocs.org/projects/imitation/badge/?version=latest)](https://imitation.readthedocs.io/en/latest/?badge=latest)
        [![codecov](https://codecov.io/gh/HumanCompatibleAI/imitation/branch/master/graph/badge.svg)](https://codecov.io/gh/HumanCompatibleAI/imitation)
        [![PyPI version](https://badge.fury.io/py/imitation.svg)](https://badge.fury.io/py/imitation)
        
        
        # Imitation Learning Baseline Implementations
        
        This project aims to provide clean implementations of imitation learning algorithms.
        Currently we have implementations of [AIRL](https://arxiv.org/abs/1710.11248) and 
        [GAIL](https://arxiv.org/abs/1606.03476), and intend to add more in the future.
        
        ### To install:
        ```
        conda create -n imitation python=3.8  # python 3.7 and virtualenv are also okay.
        conda activate imitation
        pip install -e '.[dev]'  # install `imitation` in developer mode
        ```
        
        ### Optional Mujoco Dependency:
        
        Follow instructions to install [mujoco_py v1.5 here](https://github.com/openai/mujoco-py/tree/498b451a03fb61e5bdfcb6956d8d7c881b1098b5#install-mujoco).
        
        ### 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
        ```
        View Tensorboard with `tensorboard --logdir output/`.
        
        
        # Contributing
        
        Please follow a coding style of:
          * PEP8, with line width 88.
          * Use the `black` autoformatter.
          * Follow the [Google Python Style Guide](http://google.github.io/styleguide/pyguide.html) unless
            it conflicts with the above. Examples of Google-style docstrings can be found
            [here](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html).
        
        PRs should include unit tests for any new features, and add type annotations where possible. 
        It is OK to omit annotations when it would make the code significantly more complex.
        
        We use `pytest` for unit testing: run `pytest tests/` to run the test suite.
        We use `pytype` for static type analysis.
        You should run `ci/code_checks.sh` to run linting and static type checks, and may wish
        to configure this as a Git pre-commit hook.
        
        These checks are run on CircleCI and are required to pass before merging.
        Additionally, we track test coverage by CodeCov, and mandate that code coverage
        should not decrease. This can be overridden by maintainers in exceptional cases.
        Files in `imitation/{examples,scripts}/` have no coverage requirements.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Provides-Extra: gpu
Provides-Extra: cpu
Provides-Extra: dev
Provides-Extra: test
Provides-Extra: docs
Provides-Extra: parallel
