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* Add workflow job to publish to testpypi
* Only build wheels when core tests are passing
* Change version number to match Python version spec
* Configure build steps for prereleased activity
* Move Getting Started section higher in README
* Remove the rcXX suffix from the cmake version as it doesn't like the Python versioning format
* Bump cibuildwheel version
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@@ -28,41 +28,6 @@ README](./README.nh), at [nethack.org](https://nethack.org/), and on the
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This version of NLE uses the [Farama Organisation Gymnasium Environment](https://gymnasium.farama.org) APIs.
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### NLE Language Wrapper
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We thank [ngoodger](https://github.com/ngoodger) for implementing the [NLE Language Wrapper](https://github.com/ngoodger/nle-language-wrapper) that translates the non-language observations from NetHack tasks into similar language representations. Actions can also be optionally provided in text form which are converted to the Discrete actions of the NLE.
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### NetHack Learning Dataset
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The NetHack Learning Dataset (NLD) code now ships with `NLE`, allowing users to the load large-scale datasets featured in [Dungeons and Data: A Large-Scale NetHack Dataset](), while also generating and loading their own datasets.
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```python
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import nle.dataset as nld
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ifnot nld.db.exists():
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nld.db.create()
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# NB: Different methods are used for data based on NLE and data from NAO.
For information on how to download NLD-AA and NLD-NAO, see the dataset doc [here](./DATASET.md).
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Otherwise checkout the tutorial Colab notebook [here](https://colab.research.google.com/drive/1GRP15SbOEDjbyhJGMDDb2rXAptRQztUD?usp=sharing).
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# Papers using the NetHack Learning Environment
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- Izumiya and Simo-Serra [Inventory Management with Attention-Based Meta Actions](https://esslab.jp/~ess/publications/IzumiyaCOG2021.pdf) (Waseda University, CoG 2021).
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- Samvelyan et al. [MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research](https://arxiv.org/abs/2109.13202) (FAIR, UCL, Oxford, NeurIPS 2021).
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- Zhang et al. [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/abs/2012.08621) (Berkley, FAIR, Dec 2020).
We thank [ngoodger](https://github.com/ngoodger) for implementing the [NLE Language Wrapper](https://github.com/ngoodger/nle-language-wrapper) that translates the non-language observations from NetHack tasks into similar language representations. Actions can also be optionally provided in text form which are converted to the Discrete actions of the NLE.
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### NetHack Learning Dataset
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The NetHack Learning Dataset (NLD) code now ships with `NLE`, allowing users to the load large-scale datasets featured in [Dungeons and Data: A Large-Scale NetHack Dataset](), while also generating and loading their own datasets.
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```python
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import nle.dataset as nld
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ifnot nld.db.exists():
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nld.db.create()
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# NB: Different methods are used for data based on NLE and data from NAO.
For information on how to download NLD-AA and NLD-NAO, see the dataset doc [here](./DATASET.md).
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Otherwise checkout the tutorial Colab notebook [here](https://colab.research.google.com/drive/1GRP15SbOEDjbyhJGMDDb2rXAptRQztUD?usp=sharing).
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# Papers using the NetHack Learning Environment
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- Izumiya and Simo-Serra [Inventory Management with Attention-Based Meta Actions](https://esslab.jp/~ess/publications/IzumiyaCOG2021.pdf) (Waseda University, CoG 2021).
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- Samvelyan et al. [MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research](https://arxiv.org/abs/2109.13202) (FAIR, UCL, Oxford, NeurIPS 2021).
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- Zhang et al. [BeBold: Exploration Beyond the Boundary of Explored Regions](https://arxiv.org/abs/2012.08621) (Berkley, FAIR, Dec 2020).
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