Building a smart A.I. cab
Machine Learning Engineer Nanodegree - Reinforcement Learning
This project builds a smart cab that can navigate safely and reliably in a city that has other driving agents and a regular US-based traffic system. The project leverages Q-learning as a method of training for the cab. The cab attempts many trials and earns rewards relative to the legality and efficiency of its steps and eventual destination.
Install
This project requires Python 2.7 with the pygame library installed
Code
The Q-learning 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.
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.
Techniques and concepts used
Several techniques and concepts are used in this program, including
- Machine Learning
- Q-Learning
- State mapping
- numpy
- learning rate
- logistic decline of trial rate
!