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Add new differentiable physics tutorial to readme.

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Erik Frey
2024-05-07 09:59:32 -07:00
committed by Copybara-Service
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@@ -50,16 +50,17 @@ your machine.
If you are a Python user, you might want to start with our tutorial notebooks
running on Google Colab:
- The **introductory tutorial** teaches MuJoCo basics:
- The **introductory** tutorial teaches MuJoCo basics:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb)
- The **LQR** tutorial synthesizes a linear-quadratic controller, balancing a humanoid on one leg:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/LQR.ipynb)
- The **least-squares** tutorial explains how to use the Python-based nonlinear least-squares solver:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb)
- The **MJX** tutorial provides usage examples of
[MuJoCo XLA](https://mujoco.readthedocs.io/en/stable/mjx.html), a branch of MuJoCo written in
JAX:
[MuJoCo XLA](https://mujoco.readthedocs.io/en/stable/mjx.html), a branch of MuJoCo written in JAX:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb)
- The **differentiable physics** tutorial trains locomotion policies with analytical gradients automatically derived from MuJoCo's physics step:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/training_apg.ipynb)
## Installation