Adds an initial StableBaselines3 RL environment as an example

examples/scripts/reinforcement_learning/simple_cart_pole/README.md

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+# Example for Reinforcement Learning (RL) With Gazebo
+
+This demo world shows you an example of how you can use SDFormat, Ray-RLLIB and Gazebo to perform RL with python.
+We start with a very simple cart-pole world. This world is defined in our sdf file `cart_pole.sdf`. It is analogous to
+the
+
+## Create a VENV
+
+First create a virtual environment using python,
+```
+python3 -m venv venv
+```
+Lets activate it and install rayrllib and pytorch.
+```
+. venv/bin/activate
+```
+
+Lets install our dependencies
+```
+pip install stable-baselines3[extra]
+```
+For visuallization to work you will also need to:
+```
+pip uninstall opencv-python
+pip install opencv-python-headless
+```
+This is because `opencv-python` brings in Qt5 by default.
+
+In the same terminal you should add your gazebo python install directory to the `PYTHONPATH`
+If you built gazebo from source in the current working directory this would be:
+```
+export PYTHONPATH=$PYTHONPATH:install/lib/python
+```
+
+You will also need to set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION to python due to version
+mis-matches.
+```
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+```
+
+
+## Exploring the environment
+
+You can see the environment by using `gz sim cart_pole.sdf`.