In this project, I will use reinforcement learning to train a smart cab how to drive. A smart cab is a self-driving car from the not-so-distant future that ferries people from one arbitrary location to another. It operates in an idealized grid-like city, where may be present other vehicles, but no pedestrians. Also, there is a traffic light at each intersection. This project is part of the Machine Learning Engineer Nanodegree program, from Udacity. You can check my report here and the notebook with the tests of the codes used in this project here. The TEX file was produced with help of Overleaf.
This project requires Python 2.7 and the following Python libraries installed:
In a terminal or command window, navigate to the top-level project directory smartcab/
(that contains this README) and run one of the following commands:
python smartcab/agent.py
python -m smartcab.agent
- T.M. Mitchell. Machine Learning. McGraw-Hill International Editions, 1997. link
- Udacity. Original Project Repository. 2016. link
- M. Mohri, A. Rostamizadeh, A. Talwalkar. Foundations of Machine Learning. 2012. link
- L.P. Kaelbling, M.L. Littman, A.W. Moore. Reinforcement Learning: A Survey. 1996. link
The contents of this repository are covered under the MIT License.