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Add publish workflow, first preparation for next release (#1067)
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MischaPanch authored Mar 4, 2024
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30 changes: 30 additions & 0 deletions .github/workflows/publish.yaml
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name: Upload Python Package

on:
release:
types: [created]

jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v1
with:
python-version: 3.11
# use poetry and cache installed packages, see https://github.com/marketplace/actions/python-poetry-action
- name: Install poetry
uses: abatilo/actions-poetry@v2
- name: Setup a local virtual environment (if no poetry.toml file)
run: |
poetry config virtualenvs.create true --local
poetry config virtualenvs.in-project true --local
- name: Build and publish
env:
PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
run: |
if [ -z "${PYPI_TOKEN}" ]; then echo "Set the PYPI_TOKEN variable in your repository secrets"; exit 1; fi
poetry config pypi-token.pypi $PYPI_TOKEN
poetry config repositories.pypi https://pypi.org/legacy
poetry publish --build --repository pypi
16 changes: 8 additions & 8 deletions README.md
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[![PyPI](https://img.shields.io/pypi/v/tianshou)](https://pypi.org/project/tianshou/) [![Conda](https://img.shields.io/conda/vn/conda-forge/tianshou)](https://github.com/conda-forge/tianshou-feedstock) [![Read the Docs](https://img.shields.io/readthedocs/tianshou)](https://tianshou.readthedocs.io/en/master) [![Read the Docs](https://img.shields.io/readthedocs/tianshou-docs-zh-cn?label=%E4%B8%AD%E6%96%87%E6%96%87%E6%A1%A3)](https://tianshou.readthedocs.io/zh/master/) [![Unittest](https://github.com/thu-ml/tianshou/actions/workflows/pytest.yml/badge.svg)](https://github.com/thu-ml/tianshou/actions) [![codecov](https://img.shields.io/codecov/c/gh/thu-ml/tianshou)](https://codecov.io/gh/thu-ml/tianshou) [![GitHub issues](https://img.shields.io/github/issues/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/issues) [![GitHub stars](https://img.shields.io/github/stars/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/stargazers) [![GitHub forks](https://img.shields.io/github/forks/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/network) [![GitHub license](https://img.shields.io/github/license/thu-ml/tianshou)](https://github.com/thu-ml/tianshou/blob/master/LICENSE)

> ⚠️️ **Dropped support for Gym**:
> Tianshou no longer supports Gym, and we recommend that you transition to
> [Gymnasium](http://github.com/Farama-Foundation/Gymnasium).
> If you absolutely have to use Gym, you can try using [Shimmy](https://github.com/Farama-Foundation/Shimmy)
> (the compatibility layer), but Tianshou provides no guarantees that things will work then.

> ⚠️️ **Current Status**: the Tianshou master branch is currently under heavy development,
> moving towards more features, improved interfaces, more documentation.
You can view the relevant issues in the corresponding
> [milestone](https://github.com/thu-ml/tianshou/milestone/1)
> Stay tuned! (and expect breaking changes until the release is done)
> Stay tuned! (and expect breaking changes until the next major release)
**Tianshou** ([天授](https://baike.baidu.com/item/%E5%A4%A9%E6%8E%88)) is a reinforcement learning platform based on pure PyTorch. Unlike other reinforcement learning libraries, which are partly based on TensorFlow, have unfriendly APIs ot are not optimized for speed, Tianshou provides a high-performance, modularized framework and user-friendly APIs for building deep reinforcement learning agents, enabling concise implementations without sacrificing flexibility.
**Tianshou** ([天授](https://baike.baidu.com/item/%E5%A4%A9%E6%8E%88)) is a reinforcement learning platform based on pure PyTorch and [Gymnasium](http://github.com/Farama-Foundation/Gymnasium). Unlike other reinforcement learning libraries, which may have complex codebases,
unfriendly high-level APIs, or are not optimized for speed, Tianshou provides a high-performance, modularized framework
and user-friendly interfaces for building deep reinforcement learning agents. One more aspect that sets Tianshou apart is its
generality: it supports online and offline RL, multi-agent RL, and model-based algorithms.

The set of supported algorithms includes the following:
Tianshou aims at enabling concise implementations, both for researchers and practitioners, without sacrificing flexibility.

Supported algorithms include:

- [Deep Q-Network (DQN)](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)
- [Double DQN](https://arxiv.org/pdf/1509.06461.pdf)
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10 changes: 0 additions & 10 deletions docs/04_contributing/05_contributor.rst

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28 changes: 28 additions & 0 deletions docs/04_contributing/05_contributors.rst
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Contributors
============

We always welcome contributions to help make Tianshou better!
Tiashou was originally created by the `THU-ML Group <https://ml.cs.tsinghua.edu.cn>`_ at Tsinghua University.

Today, it is backed by the `appliedAI Institute for Europe <https://www.appliedai-institute.de/en/>`_,
which is committed to making Tianshou the go-to resource for reinforcement learning research and development,
and guaranteeing its long-term maintenance and support.

The original creator Jiayi Weng (`Trinkle23897 <https://github.com/Trinkle23897>`_) continues
to be a key contributor to the project.

The current tianshou maintainers from the appliedAI Institute for Europe are:

* Michael Panchenko (`MischaPanch <https://github.com/MischaPanch>`_)
* Dominik Jain (`opcode81 <https://github.com/opcode81>`_)


An incomplete list of the early contributors is:

* Alexis Duburcq (`duburcqa <https://github.com/duburcqa>`_)
* Kaichao You (`youkaichao <https://github.com/youkaichao>`_)
* Huayu Chen (`ChenDRAG <https://github.com/ChenDRAG>`_)
* Yi Su (`nuance1979 <https://github.com/nuance1979>`_)


You can find more information about contributors `here <https://github.com/thu-ml/tianshou/graphs/contributors>`_.
4 changes: 4 additions & 0 deletions docs/spelling_wordlist.txt
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pytorch
tensordict
onwards
Dominik
Tsinghua
Tianshou
appliedAI
2 changes: 1 addition & 1 deletion pyproject.toml
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[tool.poetry]
name = "tianshou"
version = "0.5.1"
version = "1.0.0"
description = "A Library for Deep Reinforcement Learning"
authors = ["TSAIL <[email protected]>"]
license = "MIT"
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