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Merge pull request #114 from automl/development
Update to 1.1.0
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.codecov.yml

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---
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name: Issue Template
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about: General template issues
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labels:
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---
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* {{ cookiecutter.project_name }} version:
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* Python version:
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* Operating System:
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<!--
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Please file an issue for bug reports (label as `bug`), usage questions (label as `question`), feature requests (label as `feature request`), to notify us about upcoming contributions and any other topic that you think may be important discussing with us.
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-->
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#### Description
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<!-- Describe what you were trying to do -->
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#### Steps/Code to Reproduce
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<!--
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Please provide us with a minimal code example to reproduce your issue. If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com
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-->
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#### Expected Results
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<!-- Please paste or describe the expected results.-->
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#### Actual Results
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<!-- Please paste or specifically describe the actual output or traceback. If you cannot provide a full traceback, please include the kind of error you encounter as well as where it occurs in the code! -->
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#### Additional Info
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- Did you try upgrading to the most current version? yes/no
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- Are you using a supported operating system (version)? yes/no
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- How did you install this package (e.g. GitHub, pip, etc.)?
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<!-- Thanks for contributing! -->
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<!--
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Thanks for contributing a pull request! Please ensure you have taken a look at the contribution guidelines in the CONTRIBUTING.md
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-->
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#### Reference Issues/PRs
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<!--
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Example: Fixes #1234. See also #3456.
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Please use keywords (e.g., Fixes) to create link to the issues or pull requests you resolved, so that they will automatically be closed when your pull request is merged. See https://github.com/blog/1506-closing-issues-via-pull-requests
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-->
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#### What does this implement/fix? Explain your changes.
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<!--
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Good PR descriptions help us immensely in reviewing and testing your PRs. This will make merging much faster and smoother for everyone involved.
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Examples:
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**Good:** Added Documentation -- documented the learning rate feature both in the code and the docs. Also added an example.
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**Bad:** Explained learning rates
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**Good:** Dask Parallelization Backend -- parallelization is now possible with dask. This meant some restructuring of the runner backend and testing, but shouldn't change usage at all.
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**Bad:** new parallel feature
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-->
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#### Checklist
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- Are the tests passing locally? yes/no
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- Is the pre-commit passing locally? yes/no
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- Are all new features documented in code and docs? yes/no
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- Are all examples still running? yes/no
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- Are the requirements up to date? yes/no
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- Did you add yourself to the contributors in the authors file? yes/no
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#### Any other comments?
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<!--
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We appreciate your effort, but please be aware that we are not working full-time on this project. We welcome any contribution and value your effort, no matter how minor they are. If we are slow to review, either the pull request needs some benchmarking, tinkering, convincing, etc. or more likely the reviewers are simply busy. In either case, we ask for your understanding during the review process.
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Thanks for contributing!
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-->

.github/workflows/tests.yaml

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- main
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- development
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schedule:
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# Every day at 7AM UTC
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- cron: '0 07 * * *'
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env:
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# Arguments used for pytest
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strategy:
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fail-fast: false
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matrix:
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os: [windows-latest, macos-latest, ubuntu-latest]
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python-version: ['3.9', '3.10']
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kind: ['conda', 'source', 'dist']
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exclude:
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# Exclude all configurations *-*-dist, but include one later in `include`
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- kind: 'dist'
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# Exclude windows as bash commands wont work in windows runner
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- os: windows-latest
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# Exclude macos as there are permission errors using conda as we do
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- os: macos-latest
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os: [ubuntu-latest]
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python-version: ['3.9', '3.10', '3.11']
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kind: ['conda']
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include:
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# Add the tag code-cov to ubuntu-3.7-source
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- os: ubuntu-latest
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python-version: 3.9
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kind: 'source'
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kind: 'conda'
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code-cov: true
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# Include one config with dist, ubuntu-3.7-dist
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- os: ubuntu-latest
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python-version: 3.9
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kind: 'dist'
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steps:
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- name: Checkout
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# Miniconda is available in $CONDA env var
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$CONDA/bin/conda create -n testenv --yes pip wheel gxx_linux-64 gcc_linux-64 python=${{ matrix.python-version }}
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$CONDA/envs/testenv/bin/python3 -m pip install --upgrade pip
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$CONDA/envs/testenv/bin/pip3 install -e ".[dev,box2d,brax,dm_control,mario]"
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- name: Source install
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if: matrix.kind == 'source'
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run: |
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python -m pip install --upgrade pip
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pip install -e ".[dev,box2d,brax,dm_control,mario]"
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- name: Dist install
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if: matrix.kind == 'dist'
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run: |
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python -m pip install --upgrade pip
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python setup.py sdist
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last_dist=$(ls -t dist/carl-*.tar.gz | head -n 1)
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pip install $last_dist[dev,box2d,brax,dm_control,mario]
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$CONDA/envs/testenv/bin/pip3 install -e .[dev,dm_control,mario,brax,box2d]
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- name: Tests
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timeout-minutes: 60
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else
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$PYTHON -m pytest ${{ env.pytest-args }} --ignore=test/local_only test
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fi
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- name: Upload coverage
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if: matrix.code-cov && always()
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uses: codecov/codecov-action@v2
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with:
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fail_ci_if_error: true
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verbose: true

.readthedocs.yaml

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CITATION.bib

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CITATION.cff

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cff-version: 1.2.0
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message: "If you use this software, please cite our paper:"
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url: "https://automl.github.io/CARL/"
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repository-code: "https://github.com/automl/CARL"
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title: "CARL - Context Adaptive Reinforcement Learning"
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authors:
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- family-names: "Benjamins"
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given-names: "Carolin"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Eimer"
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given-names: "Theresa"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Schubert"
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given-names: "Frederik"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Mohan"
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given-names: "Aditya"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Döhler"
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given-names: "Sebastian"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Biedenkapp"
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given-names: "André"
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affiliation: "Albert-Ludwigs University Freiburg, Germany"
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- family-names: "Rosenhahn"
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given-names: "Bodo"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Hutter"
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given-names: "Frank"
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affiliation: "Albert-Ludwigs University Freiburg, Germany"
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- family-names: "Lindauer"
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given-names: "Marius"
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affiliation: "Leibniz University Hannover, Germany"
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preferred-citation:
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type: "article"
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title: "Contextualize Me - The Case for Context in Reinforcement Learning"
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year: 2023
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journal: "Transactions on Machine Learning Research"
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authors:
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- family-names: "Benjamins"
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given-names: "Carolin"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Eimer"
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given-names: "Theresa"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Schubert"
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given-names: "Frederik"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Mohan"
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given-names: "Aditya"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Döhler"
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given-names: "Sebastian"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Biedenkapp"
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given-names: "André"
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affiliation: "Albert-Ludwigs University Freiburg, Germany"
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- family-names: "Rosenhahn"
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given-names: "Bodo"
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affiliation: "Leibniz University Hannover, Germany"
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- family-names: "Hutter"
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given-names: "Frank"
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affiliation: "Albert-Ludwigs University Freiburg, Germany"
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- family-names: "Lindauer"
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given-names: "Marius"
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affiliation: "Leibniz University Hannover, Germany"

Makefile

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$(PIP) install -e ".[dev, docs]"
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pre-commit install
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install:
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$(PIP) install -e .
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$(BLACK) carl test --check || :
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format-black:
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$(BLACK) carl test
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$(BLACK) carl test examples
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$(ISORT) carl test
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test:
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$(PYTEST) test
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$(PYTEST) --disable-warnings test
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cov-report:
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coverage html -d coverage_html
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$(MAKE) -C ${DOCDIR} clean

README.md

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<img align="left" width="80" src="./docs/source/figures/CARL_logo.png" alt="CARL">
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# – The Benchmark Library
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[![PyPI Version](https://img.shields.io/pypi/v/carl-bench.svg)](https://pypi.python.org/pypi/carl-bench)
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[![Test](https://github.com/automl/carl/actions/workflows/tests.yaml/badge.svg)](https://github.com/automl/carl/actions/workflows/tests.yaml)
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[![Doc Status](https://github.com/automl/carl/actions/workflows/docs.yaml/badge.svg)](https://github.com/automl/carl/actions/workflows/docs.yaml)
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
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CARL (context adaptive RL) provides highly configurable contextual extensions
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to several well-known RL environments.
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It's designed to test your agent's generalization capabilities

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