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, Stable Baselines 3 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 cart-pole world in gymnasium.
+
+## Create a VENV
+
+First create a virtual environment using python,
+```
+python3 -m venv venv
+```
+Let's activate it and install stablebaselines3 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:
+```
+# cd to the colcon workspace where you built gazebo
+cd <path_to_colcon_workspace>
+export PYTHONPATH=$PYTHONPATH:`pwd`/install/lib/python
+. install/setup.bash
+```
+
+You will also need to set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION to python due to version
+mismatches.
+```
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+```
+
+
+## Exploring the environment
+
+You can see the environment by using `gz sim cart_pole.sdf`.
+
+## Perform RL
+
+To perform RL take a look at `cart_pole_env.py`. We simply subclass `gym.Env` and
+create a new gazebo system. Close any instance of gazebo you may be running.
+To run the script, in your terminal with the venv sourced run:
+```
+python3 cart_pole_env.py
+```