Add introduction section

doc/tutorial.rst

@@ -4,6 +4,58 @@ Tutorial
 Introduction
 ------------
 
+This tutorial focuses on functionality provided by ``dm_robotics_panda``.
+Although the examples provide a fully implemented starting point, for more details
+on how to prepare reinforcement learning environments please refer to the material
+provided with the `dm_robotics <https://github.com/google-deepmind/dm_robotics>`_
+and by extension `dm_control <https://github.com/google-deepmind/dm_control>`_ repositories.
+
+.. note::
+    This section is implemented in ``examples/minimal_working_example.py``.
+    Run this file or any of the examples with the ``--gui`` option or ``-g``
+    to run the simulation in the visualization app.
+
+A minimum working example consists of a robot configuration, an environment,
+an agent that controls the robot, and a method to run the environment.
+:py:class:`dm_robotics.panda.environment.PandaEnvironment` is the main point of interaction.
+This class populates the environment with Panda robots configured using
+:py:class:`dm_robotics.panda.parameters.RobotParams`.
+
+.. code:: python
+
+   robot_params = params.RobotParams()
+   panda_env = environment.PandaEnvironment(robot_params)
+
+   with panda_env.build_task_environment() as env:
+     ...
+
+The MoMa task environment returned by ``build_task_environment()`` can then be executed
+by :py:func:`dm_robotics.panda.run_loop.run` or visualized with 
+:py:class:`dm_robotics.panda.utils.ApplicationWithPlot`. The visualization app
+is a convenient tool to debug environments or evaluate agents and
+includes live plots of observations, actions, and rewards.
+
+.. image:: img/gui.png
+   :alt: Visualization App
+
+Finally, we require an agent that provides actions in the form of NumPy arrays
+according to the environment's specification. A minimal agent provides a ``step``
+function that receives a timestep and returns an action.
+
+.. code:: python
+
+   class Agent:
+
+     def __init__(self, spec: specs.BoundedArray) -> None:
+       self._spec = spec
+
+     def step(self, timestep: dm_env.TimeStep) -> np.ndarray:
+       action = np.zeros(shape=self._spec.shape, dtype=self._spec.dtype)
+       return action
+
+The shape of the action will depend on the number of configured robots and chosen actuation mode.
+
+
 Motion Control
 --------------