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@@ -21,23 +21,23 @@ MiniHack comes with a large list of challenging [environments](https://minihack.
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The motivation behind MiniHack is to be able to perform RL experiments in a controlled setting while being able to increasingly scale the complexity of the tasks.
To do this, MiniHack leverages the so-called [description files](https://nethackwiki.com/wiki/Des-file_format) written using a human-readable probabilistic-programming-like domain-specific language. With just a few lines of code, people can generate a large variety of [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) environments, controlling every little detail, from the location and types of monsters, to the traps, objects, and terrain of the level, all while introducing randomness that challenges generalization capabilities of RL agents. For further details, we refer users to our [brief overview](https://minihack.readthedocs.io/en/latest/getting-started/des_files.html), [detailed tutorial](https://minihack.readthedocs.io/en/latest/tutorials/des_file_tutorial.html), or [interactive notebook](./docs/tutorials/des_file_tutorial.ipynb).
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[Our documentation](https://minihack.readthedocs.io/) will walk you through everything you need to know about MiniHack, step-by-step, including information on how to get started, configure environments or design new ones, train baseline agents, and much more.
The [MiniHack Level Editor](https://minihack-editor.github.io) allows to easily define MiniHack environments inside a browser using a convenient drag-and-drop functionality. The source code is available [here](https://github.com/minihack-editor/minihack-editor.github.io).
@@ -72,7 +72,7 @@ We thank [ngoodger](https://github.com/ngoodger) for implementing the [NLE Langu
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- Parker-Holder et al. [That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities](https://openreview.net/forum?id=3qGInPFqR0p) (Oxford, FAIR, UCL, Berkeley, DeepRL Workshop 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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Open a [pull request](https://github.com/facebookresearch/minihack/edit/main/README.md) to add papers.
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Open a [pull request](https://github.com/samvelyan/minihack/edit/main/README.md) to add papers.
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# Installation
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If you wish to extend MiniHack, please install the package as follows:
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