.. _tutorial-create-base-env:


Creating a Base Environment
===========================

.. currentmodule:: omni.isaac.orbit

Environments bring together different aspects of the simulation such as
the scene, observations and actions spaces, randomizations, etc. to create a
coherent interface for various applications. In Orbit, environments are
implemented as :class:`envs.BaseEnv` and :class:`envs.RLTaskEnv` classes.
The two classes are very similar, but :class:`envs.RLTaskEnv` is useful for
reinforcement learning tasks and contains rewards, terminations, curriculum
and command generation. The :class:`envs.BaseEnv` class is useful for
traditional robot control and doesn't contain rewards and terminations.

In this tutorial, we will look at the base class :class:`envs.BaseEnv` and its
corresponding configuration class :class:`envs.BaseEnvCfg`. We will use the
cartpole environment from earlier to illustrate the different components
in creating a new :class:`envs.BaseEnv` environment.


The Code
~~~~~~~~

The tutorial corresponds to the ``create_cartpole_base_env`` script  in the ``orbit/source/standalone/tutorials/03_envs``
directory.

.. dropdown:: Code for create_cartpole_base_env.py
   :icon: code

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      :language: python
      :emphasize-lines: 51-55, 58-75, 78-111, 114-133, 138-142, 147, 151, 156-157, 163-164
      :linenos:


The Code Explained
~~~~~~~~~~~~~~~~~~

The base class :class:`envs.BaseEnv` wraps around many intricacies of the simulation interaction
and provides a simple interface for the user to run the simulation and interact with it. It
is composed of the following components:

* :class:`scene.InteractiveScene` - The scene that is used for the simulation.
* :class:`managers.ActionManager` - The manager that handles actions.
* :class:`managers.ObservationManager` - The manager that handles observations.
* :class:`managers.RandomizationManager` - The manager that handles randomizations.

By configuring these components, the user can create different variations of the same environment
with minimal effort. In this tutorial, we will go through the different components of the
:class:`envs.BaseEnv` class and how to configure them to create a new environment.

Designing the scene
-------------------

The first step in creating a new environment is to configure its scene. For the cartpole
environment, we will be using the scene from the previous tutorial. Thus, we omit the
scene configuration here. For more details on how to configure a scene, see
:ref:`tutorial-interactive-scene`.

Defining actions
----------------

In the previous tutorial, we directly input the action to the cartpole using
the :meth:`assets.Articulation.set_joint_effort_target` method. In this tutorial, we will
use the :class:`managers.ActionManager` to handle the actions.

The action manager can comprise of multiple :class:`managers.ActionTerm`. Each action term
is responsible for applying *control* over a specific aspect of the environment. For instance,
for robotic arm, we can have two action terms -- one for controlling the joints of the arm,
and the other for controlling the gripper. This composition allows the user to define
different control schemes for different aspects of the environment.

In the cartpole environment, we want to control the force applied to the cart to balance the pole.
Thus, we will create an action term that controls the force applied to the cart.

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   :language: python
   :pyobject: ActionsCfg
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Defining observations
---------------------

While the scene defines the state of the environment, the observations define the states
that are observable by the agent. These observations are used by the agent to make decisions
on what actions to take. In Orbit, the observations are computed by the
:class:`managers.ObservationManager` class.

Similar to the action manager, the observation manager can comprise of multiple observation terms.
These are further grouped into observation groups which are used to define different observation
spaces for the environment. For instance, for hierarchical control, we may want to define
two observation groups -- one for the low level controller and the other for the high level
controller. It is assumed that all the observation terms in a group have the same dimensions.

For this tutorial, we will only define one observation group named ``"policy"``. While not completely
prescriptive, this group is a necessary requirement for various wrappers in Orbit.
We define a group by inheriting from the :class:`managers.ObservationGroupCfg` class. This class
collects different observation terms and help define common properties for the group, such
as enabling noise corruption or concatenating the observations into a single tensor.

The individual terms are defined by inheriting from the :class:`managers.ObservationTermCfg` class.
This class takes in the :attr:`managers.ObservationTermCfg.func` that specifies the function or
callable class that computes the observation for that term. It includes other parameters for
defining the noise model, clipping, scaling, etc. However, we leave these parameters to their
default values for this tutorial.

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   :language: python
   :pyobject: ObservationsCfg
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Defining randomizations
-----------------------

At this point, we have defined the scene, actions and observations for the cartpole environment.
The general idea for all these components is to define the configuration classes and then
pass them to the corresponding managers. The randomization manager is no different.

The :class:`managers.RandomizationManager` class is responsible for randomizing everything related
to the simulation state. This includes randomizing (or resetting) the scene, randomizing physical
properties (such as mass, friction, etc.), and visual properties (such as colors, textures, etc.).
Each of these are specified through the :class:`managers.RandomizationTermCfg` class, which
takes in the :attr:`managers.RandomizationTermCfg.func` that specifies the function or callable
class that performs the randomization. Additionally, it expects the **mode** of randomization.
The mode specifies when the randomization term should be applied. It is possible to specify
your own mode, but Orbit provides three modes out of the box:

* ``"startup"`` - Randomize only once at environment startup.
* ``"reset"`` - Randomize on every call to an environment's reset.
* ``"interval"`` - Randomize at a given fixed interval.

For this example, we randomize the pole's mass on startup. This is done only once since this operation
is expensive and we don't want to do it on every reset. We also randomize the initial joint state of
the cartpole and the pole at every reset.

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   :language: python
   :pyobject: RandomizationCfg
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Tying it all together
---------------------

Having defined the scene and manager configurations, we can now define the environment configuration
through the :class:`envs.BaseEnvCfg` class. This class takes in the scene, action, observation and
randomization configurations.

In addition to these, it also takes in the :attr:`envs.BaseEnvCfg.sim` which defines the simulation
parameters such as the timestep, gravity, etc. This is initialized to the default values, but can
be modified as needed. We recommend doing so by defining the :meth:`__post_init__` method in the
:class:`envs.BaseEnvCfg` class, which is called after the configuration is initialized.

.. literalinclude:: ../../../../source/standalone/tutorials/03_envs/create_cartpole_base_env.py
   :language: python
   :pyobject: CartpoleEnvCfg
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   :lineno-start: 114

Running the simulation
----------------------

Lastly, we revisit the simulation execution loop. This is now much simpler since we have
abstracted away most of the details into the environment configuration. We only need to
call the :meth:`envs.BaseEnv.reset` method to reset the environment and :meth:`envs.BaseEnv.step`
method to step the environment. Both these functions return the observation and an info dictionary
which may contain additional information provided by the environment. These can be used by an
agent for decision-making.

The :class:`envs.BaseEnv` class does not have any notion of terminations since that concept is
specific for episodic tasks. Thus, the user is responsible for defining the termination condition
for the environment. In this tutorial, we reset the simulation at regular intervals.

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   :language: python
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An important thing to note above is that the entire simulation loop is wrapped inside the
:meth:`torch.inference_mode` context manager. This is because the environment uses PyTorch
operations under-the-hood and we want to ensure that the simulation is not slowed down by
the overhead of PyTorch's autograd engine and gradients are not computed for the simulation
operations.


The Code Execution
~~~~~~~~~~~~~~~~~~

To run the base environment made in this tutorial, you can use the following command:

.. code-block:: bash

   ./orbit.sh -p source/standalone/tutorials/03_envs/create_cartpole_base_env.py --num_envs 32


This should open a stage with a ground plane, light source, and cartpoles. The simulation should be
playing with random actions on the cartpole. Additionally, it opens a UI window on the bottom
right corner of the screen named ``"Orbit"``. This window contains different UI elements that
can be used for debugging and visualization.

To stop the simulation, you can either close the window, or press ``Ctrl+C`` in the terminal where you
started the simulation.

In this tutorial, we learned about the different managers that help define a base environment. We
include more examples of defining the base environment in the ``orbit/source/standalone/tutorials/03_envs``
directory. For completion, they can be run using the following commands:

.. code-block:: bash

   # Floating cube environment with custom action term for PD control
   ./orbit.sh -p source/standalone/tutorials/03_envs/create_cube_base_env.py --num_envs 32

   # Quadrupedal locomotion environment with a policy that interacts with the environment
   ./orbit.sh -p source/standalone/tutorials/03_envs/create_quadruped_base_env.py --num_envs 32


In the following tutorial, we will look at the :class:`envs.RLTaskEnv` class and how to use it
to create a Markovian Decision Process (MDP).
