seals 0.2.1
Suite of Environments for Algorithms that Learn Specifications
License: MIT
Requires Python: >=3.8.0

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**Status**: early beta.

*seals*, the Suite of Environments for Algorithms that Learn Specifications, is a toolkit for
evaluating specification learning algorithms, such as reward or imitation learning. The
environments are compatible with [Gym](https://github.com/openai/gym), but are designed
to test algorithms that learn from user data, without requiring a procedurally specified
reward function.

There are two types of environments in *seals*:

  - **Diagnostic Tasks** which test individual facets of algorithm performance in isolation.
  - **Renovated Environments**, adaptations of widely-used benchmarks such as MuJoCo continuous
      control tasks and Atari games to be suitable for specification learning benchmarks. In particular, 
      we remove any side-channel sources of reward information from MuJoCo tasks, and give Atari games constant-length episodes (although most Atari environments have observations that include the score).

*seals* is under active development and we intend to add more categories of tasks soon.
 
You may also be interested in our sister project [imitation](https://github.com/humancompatibleai/imitation/),
providing implementations of a variety of imitation and reward learning algorithms.

Check out our [documentation](https://seals.readthedocs.io/en/latest/) for more information about *seals*.

# Quickstart

To install the latest release from PyPI, run:
 
```bash
pip install seals
```

All *seals* environments are available in the Gym registry. Simply import it and then use as you
would with your usual RL or specification learning algroithm:

```python
import gymnasium as gym
import seals

env = gym.make('seals/CartPole-v0')
```

We make releases periodically, but if you wish to use the latest version of the code, you can
install directly from Git master:

```bash
pip install git+https://github.com/HumanCompatibleAI/seals.git
```

# Contributing

For development, clone the source code and create a virtual environment for this project:

```bash
git clone git@github.com:HumanCompatibleAI/seals.git
cd seals
./ci/build_venv.sh
pip install -e .[dev]  # install extra tools useful for development
```

## Code style

We follow a PEP8 code style with line length 88, and typically follow the [Google Code Style Guide](http://google.github.io/styleguide/pyguide.html),
but defer to PEP8 where they conflict. We use the `black` autoformatter to avoid arguing over formatting.
Docstrings follow the Google docstring convention defined [here](http://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings),
with an extensive example in the [Sphinx docs](https://www.sphinx-doc.org/en/master/usage/extensions/example_google.html).

All PRs must pass linting via the `ci/code_checks.sh` script. It is convenient to install this as a commit hook:

```bash
ln -s ../../ci/code_checks.sh .git/hooks/pre-commit
```

## Tests

We use [pytest](https://docs.pytest.org/en/latest/) for unit tests
and [codecov](http://codecov.io/) for code coverage.
We also use [pytype](https://github.com/google/pytype) and [mypy](http://mypy-lang.org/)
for type checking.

## Workflow

Trivial changes (e.g. typo fixes) may be made directly by maintainers. Any non-trivial changes
must be proposed in a PR and approved by at least one maintainer. PRs must pass the continuous 
integration tests (CircleCI linting, type checking, unit tests and CodeCov) to be merged.

It is often helpful to open an issue before proposing a PR, to allow for discussion of the design
before coding commences.

# Citing seals

To cite this project in publications:

```bibtex
 @misc{seals,
   author = {Adam Gleave and Pedro Freire and Steven Wang and Sam Toyer},
   title = {{seals}: Suite of Environments for Algorithms that Learn Specifications},
   year = {2020},
   publisher = {GitHub},
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/HumanCompatibleAI/seals}},
}
```