Publicize the rollout notebook, small improvements to plotting.
PiperOrigin-RevId: 730841667 Change-Id: Id365f21cee078f28cb3fa797701c3c5a409607cd
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Yuval Tassa
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Copybara-Service
Copybara-Service
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ce82b63155
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dc98ae978d
@@ -52,6 +52,8 @@ running on Google Colab:
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- The **introductory** tutorial teaches MuJoCo basics:
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[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb)
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- The **rollout** tutorial shows how to use the multithreaded `rollout` module:
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[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb)
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- The **LQR** tutorial synthesizes a linear-quadratic controller, balancing a humanoid on one leg:
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[](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/LQR.ipynb)
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- The **least-squares** tutorial explains how to use the Python-based nonlinear least-squares solver:
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@@ -736,15 +736,24 @@ The ``mujoco`` package contains two sub-modules: ``mujoco.rollout`` and ``mujoco
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rollout
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-------
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``mujoco.rollout`` and ``mujoco.rollout.Rollout`` shows how to add additional C/C++ functionality, exposed as a Python
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module via pybind11. It is implemented in `rollout.cc
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<https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.cc>`__ and wrapped in `rollout.py
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<https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.py>`__. The module performs a common
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functionality where tight loops implemented outside of Python are beneficial: rolling out a trajectory (i.e., calling
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<https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.py>`__. The module addresses a common
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use-case where tight loops implemented outside of Python are beneficial: rolling out a trajectory (i.e., calling
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:ref:`mj_step` in a loop), given an initial state and sequence of controls, and returning subsequent states and sensor
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values. The rollouts are run in parallel with an internally managed thread pool if multiple MjData instances (one per
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thread) are passed as an argument. The basic usage form is
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thread) are passed as an argument. This notebook shows how to use ``rollout`` |rollout_colab|, along with some
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benchmarks e.g., the figure below.
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.. |rollout_colab| image:: https://colab.research.google.com/assets/colab-badge.svg
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:target: https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb
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.. image:: images/python/rollout.png
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:align: right
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:width: 97%
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The basic usage form is
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.. code-block:: python
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"id": "0f9fbad1-59d0-40ac-b2b6-99f37313670f",
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"metadata": {
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"editable": true,
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"editable": true,
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"metadata": {
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"id": "9cd2f94a-11df-4247-986c-5a56af69a1f5"
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"id": "957b8566-da31-410b-b385-e78241c5247a"
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"id": "b8a5d3d4-24e7-41a1-b3bd-7b63c1812b03"
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"id": "2a184873-8d24-45da-b444-8d21f5dcd733"
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"id": "4b02bb61-912d-47de-a956-aadfcd4c5cd5",
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"id": "4b02bb61-912d-47de-a956-aadfcd4c5cd5"
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"id": "d02cc8e8-63cd-4852-ab3c-364a18025a95",
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"cellView": "form",
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@@ -1253,6 +1253,7 @@
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" ax1.set_xticks(x + width, nbatch)\n",
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" ax1.yaxis.set_major_formatter(ticker)\n",
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" ax1.grid()\n",
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" ax1.set_axisbelow(True)\n",
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" ax1.set_xlabel('nbatch')\n",
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" ax1.set_ylabel('steps per second')\n",
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" ax1.set_title(f'nbatch varied, nstep = {nominal_nstep}')\n",
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" ax2.set_xticks(x + width, nstep)\n",
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" ax2.yaxis.set_major_formatter(ticker)\n",
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" ax2.grid()\n",
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" ax2.set_axisbelow(True)\n",
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" ax2.set_xlabel('nstep')\n",
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" ax2.set_title(f'nstep varied, nbatch = {nominal_nbatch}')\n",
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"\n",
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" ax2.legend(loc=(1.04, 0.0))\n",
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" fig.set_size_inches(10, 4)\n",
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" ax1.legend(loc=(0.03, 0.8))\n",
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" fig.set_size_inches(10, 5)\n",
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" plt.suptitle(title)\n",
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" plt.tight_layout()"
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]
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