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align=\"center\"\u003e\n  \u003cb\u003eyarllib\u003c/b\u003e\n\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://pypi.org/project/yarllib\"\u003e\n    \u003cimg alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/yarllib\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/yarllib\"\u003e\n    \u003cimg alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/yarllib\" /\u003e\n  \u003c/a\u003e\n  \u003ca href=\"\"\u003e\n    \u003cimg alt=\"PyPI - Status\" src=\"https://img.shields.io/pypi/status/yarllib\" /\u003e\n  \u003c/a\u003e\n  \u003ca href=\"\"\u003e\n    \u003cimg alt=\"PyPI - Implementation\" src=\"https://img.shields.io/pypi/implementation/yarllib\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"\"\u003e\n    \u003cimg alt=\"PyPI - Wheel\" src=\"https://img.shields.io/pypi/wheel/yarllib\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://github.com/marcofavorito/yarllib/blob/master/LICENSE\"\u003e\n    \u003cimg alt=\"GitHub\" src=\"https://img.shields.io/github/license/marcofavorito/yarllib\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"\"\u003e\n    \u003cimg alt=\"test\" src=\"https://github.com/marcofavorito/yarllib/workflows/test/badge.svg\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"\"\u003e\n    \u003cimg alt=\"lint\" src=\"https://github.com/marcofavorito/yarllib/workflows/lint/badge.svg\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"\"\u003e\n    \u003cimg alt=\"docs\" src=\"https://github.com/marcofavorito/yarllib/workflows/docs/badge.svg\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://codecov.io/gh/marcofavorito/yarllib\"\u003e\n    \u003cimg alt=\"codecov\" src=\"https://codecov.io/gh/marcofavorito/yarllib/branch/master/graph/badge.svg?token=FG3ATGP5P5\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://img.shields.io/badge/flake8-checked-blueviolet\"\u003e\n    \u003cimg alt=\"\" src=\"https://img.shields.io/badge/flake8-checked-blueviolet\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://img.shields.io/badge/mypy-checked-blue\"\u003e\n    \u003cimg alt=\"\" src=\"https://img.shields.io/badge/mypy-checked-blue\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://img.shields.io/badge/code%20style-black-black\"\u003e\n    \u003cimg alt=\"black\" src=\"https://img.shields.io/badge/code%20style-black-black\" /\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://www.mkdocs.org/\"\u003e\n    \u003cimg alt=\"\" src=\"https://img.shields.io/badge/docs-mkdocs-9cf\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\nYet Another Reinforcement Learning Library.\n\nStatus: **development**.\n\n## Why?\n\nI had the need for a RL library/framework that:\n- was clearly and simply implemented, with good enough performances;\n- highly focused on modularity, customizability and extendability;\n- wasn't merely Deep Reinforcement Learning oriented.\n\nI couldn't find an existing library that satisfied my needs; \nhence I decided to implement _yet another_ RL library.\n\nFor me it is also an opportunity to \nhave a better understanding of the RL algorithms\nand to appreciate the nuances that you can't find on a book.\n\nIf you find this repo useful for your research or your project,\nI'd be very glad :-) don't hesitate to reach me out!\n\n## What\n\nThe package is both:\n- a _library_, because it provides off-the-shelf functionalities to\n  set up an RL experiment;\n- a _framework_, because you can compose your custom model by implementing\n  the interfaces, override the default behaviours, or use the existing\n  components as-is.   \n\nYou can find more details in the \n[documentation](https://marcofavorito.github.io/yarllib).\n\n## Tests\n\nTo run tests: `tox`\n\nTo run only the code tests: `tox -e py3.7`\n\nTo run only the linters: \n- `tox -e flake8`\n- `tox -e mypy`\n- `tox -e black-check`\n- `tox -e isort-check`\n\nPlease look at the `tox.ini` file for the full list of supported commands. \n\n## Docs\n\nTo build the docs: `mkdocs build`\n\nTo view documentation in a browser: `mkdocs serve`\nand then go to [http://localhost:8000](http://localhost:8000)\n\n## License\n\nyarllib is released under the GNU Lesser General Public License v3.0 or later (LGPLv3+).\n\nCopyright 2020 Marco Favorito\n\n## Authors\n\n- [Marco Favorito](https://marcofavorito.github.io/)\n\n## Cite\n\nIf you use this library for your research, please consider citing this repository:\n\n```\n@misc{favorito2020,\n  Author = {Marco Favorito},\n  Title = {yarllib: Yet Another Reinforcement Learning Library},\n  Year = {2020},\n}\n```\nAn e-print will come soon :-) \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarfvr%2Fyarllib","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarfvr%2Fyarllib","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarfvr%2Fyarllib/lists"}