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Fix documentation typos.

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Change-Id: I176db4d4168c37b2819df14b439c4cde838e2025
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Yuval Tassa
2025-01-27 10:57:06 -08:00
committed by Copybara-Service
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@@ -718,31 +718,34 @@ The ``mujoco`` package contains two sub-modules: ``mujoco.rollout`` and ``mujoco
rollout
-------
``mujoco.rollout`` and ``mujoco.rollout.Rollout`` shows how to add additional C/C++ functionality, exposed as a Python module
via pybind11. It is implemented in `rollout.cc <https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.cc>`__
and wrapped in `rollout.py <https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.py>`__. The module
performs a common functionality where tight loops implemented outside of Python are beneficial: rolling out a trajectory
(i.e., calling :ref:`mj_step` in a loop), given an intial state and sequence of controls, and returning subsequent
states and sensor values. The rollouts are run in parallel with an internally managed thread pool if multiple MjData instances
(one per thread) are passed as an argument. The basic usage form is
``mujoco.rollout`` and ``mujoco.rollout.Rollout`` shows how to add additional C/C++ functionality, exposed as a Python
module via pybind11. It is implemented in `rollout.cc
<https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.cc>`__ and wrapped in `rollout.py
<https://github.com/google-deepmind/mujoco/blob/main/python/mujoco/rollout.py>`__. The module performs a common
functionality where tight loops implemented outside of Python are beneficial: rolling out a trajectory (i.e., calling
:ref:`mj_step` in a loop), given an initial state and sequence of controls, and returning subsequent states and sensor
values. The rollouts are run in parallel with an internally managed thread pool if multiple MjData instances (one per
thread) are passed as an argument. The basic usage form is
.. code-block:: python
state, sensordata = rollout.rollout(model, data, initial_state, control)
``model`` is either a single instance of MjModel or a sequence of compatible MjModel of length ``nbatch``.
``data`` is either a single instance of MjData or a sequence of compatible MjData of length ``nthread``.
``initial_state`` is an ``nbatch x nstate`` array, with ``nbatch`` initial states of size ``nstate``, where
``nstate = mj_stateSize(model, mjtState.mjSTATE_FULLPHYSICS)`` is the size of the
:ref:`full physics state<geFullPhysics>`. ``control`` is a ``nbatch x nstep x ncontrol`` array of controls. Controls are
by default the ``mjModel.nu`` standard actuators, but any combination of :ref:`user input<geInput>` arrays can be
specified by passing an optional ``control_spec`` bitflag.
- ``model`` is either a single instance of MjModel or a sequence of homogeneous MjModels of length ``nbatch``.
Homogeneous models have the same integer sizes, but floating point values can differ.
- ``data`` is either a single instance of MjData or a sequence of compatible MjDatas of length ``nthread``.
- ``initial_state`` is an ``nbatch x nstate`` array, with ``nbatch`` initial states of size ``nstate``, where
``nstate = mj_stateSize(model, mjtState.mjSTATE_FULLPHYSICS)`` is the size of the
:ref:`full physics state<geFullPhysics>`.
- ``control`` is a ``nbatch x nstep x ncontrol`` array of controls. Controls are by default the ``mjModel.nu`` standard
actuators, but any combination of :ref:`user input<geInput>` arrays can be specified by passing an optional
``control_spec`` bitflag.
If a rollout diverges, the current state and sensor values are used to fill the remainder of the trajectory.
Therefore, non-increasing time values can be used to detect diverged rollouts.
The ``rollout`` function is designed to be computationally stateless, so all inputs of the stepping pipeline are set and any
values already present in the given ``MjData`` instance will have no effect on the output.
The ``rollout`` function is designed to be computationally stateless, so all inputs of the stepping pipeline are set and
any values already present in the given ``MjData`` instance will have no effect on the output.
By default ``rollout.rollout`` creates a new thread pool every call if ``len(data) > 1``. To reuse the thread pool
over multiple calls use the ``persistent_pool`` argument. ``rollout.rollout`` is not thread safe when using