Reinforcement Learning project for Udacity's Machine Learning Nanodegree

• Language:- Python 2.7

  Python libraries:

  • NumPy
  • pandas
  • matplotlib
  • Pygame (optional)

  Additional software:

  • Jupyter Notebook to run IPython notebooks.

The original template and supporting files are taken from projects/smartcab folder of Udacity's machine-learning repository on GitHub.

Review: Smartcab rubric

The smartcab operates in an ideal, grid-like city (similar to New York City), with roads going in the North-South and East-West directions. Other vehicles will certainly be present on the road, but there will be no pedestrians to be concerned with. At each intersection there is a traffic light that either allows traffic in the North-South direction or the East-West direction. U.S. Right-of-Way rules apply:

• On a green light, a left turn is permitted if there is no oncoming traffic making a right turn or coming straight through the intersection.
• On a red light, a right turn is permitted if no oncoming traffic is approaching from your left through the intersection. To understand how to correctly yield to oncoming traffic when turning left, you may refer to this official drivers' education video, or this passionate exposition.

In a terminal or command window, navigate to the folder smartcab/ in project directory and run one of the following commands:

jupyter notebook smartcab.ipynb

or

ipython notebook smartcab.ipynb

This will open the Jupyter Notebook software and project file in your web browser.

To run the smartcab agent:-

python smartcab/agent.py or
python -m smartcab.agent