Machine Learning Engineer Nanodegree

Reinforcement Learning

Project: Train a Smartcab How to Drive

General Info

The world and Q-learing analysis is on the smartcab.ipynb along some visuals

To see the implmentation of Q-learning in this environment, see the agent.py file inside the smartcab folder

Install

This project requires Python 2.7 with the pygame library installed

Code

Template code is provided in the smartcab/agent.py python file. Additional supporting python code can be found in smartcab/enviroment.py, smartcab/planner.py, and smartcab/simulator.py. Supporting images for the graphical user interface can be found in the images folder. While some code has already been implemented to get you started, you will need to implement additional functionality for the LearningAgent class in agent.py when requested to successfully complete the project.

Run

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

This will run the agent.py file and execute your agent code.