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[Docs] Pointers to BenchMARL #1710

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[Docs] Pointers to BenchMARL #1710

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6 changes: 6 additions & 0 deletions README.md
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Expand Up @@ -683,6 +683,12 @@ As mentioned above, a list of open contributions can be found in [here](https://

Contributors are recommended to install [pre-commit hooks](https://pre-commit.com/) (using `pre-commit install`). pre-commit will check for linting related issues when the code is committed locally. You can disable th check by appending `-n` to your commit command: `git commit -m <commit message> -n`

## Ecosystem

Here are some projects that are part of the TorchRL ecosystem:
- [BenchMARL](https://github.com/facebookresearch/BenchMARL): a benchmarking library where you can train and compare MARL algorithms, tasks, and models using TorchRL!



## Disclaimer

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6 changes: 6 additions & 0 deletions examples/multiagent/README.md
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Expand Up @@ -10,6 +10,12 @@ In this folder we provide a set of multi-agent example scripts using the [VMAS](

For more details on the experiment setup and the environments please refer to the corresponding section of the appendix in the [TorchRL paper](https://arxiv.org/abs/2306.00577).

> [!NOTE]
> We have just released [BenchMARL](https://github.com/facebookresearch/BenchMARL), a benchmarking library where you
> can train and compare MARL algorithms, tasks, and models using TorchRL!
> Check out [how easy it is to use it.](https://colab.research.google.com/github/facebookresearch/BenchMARL/blob/main/notebooks/run.ipynb)


## Using the scripts

### Install
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7 changes: 7 additions & 0 deletions tutorials/sphinx-tutorials/multiagent_ppo.py
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===============================================================
**Author**: `Matteo Bettini <https://github.com/matteobettini>`_

.. note::

We have just released `BenchMARL <https://github.com/facebookresearch/BenchMARL>`__, a benchmarking library where you
can train and compare MARL algorithms, tasks, and models using TorchRL!
Check out `how easy it is to use it <https://colab.research.google.com/github/facebookresearch/BenchMARL/blob/main/notebooks/run.ipynb>`__.


This tutorial demonstrates how to use PyTorch and :py:mod:`torchrl` to
solve a Multi-Agent Reinforcement Learning (MARL) problem.

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