daoos/udacity_mlnd_smartcab

Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.

Train a Smartcab How to Drive

Reinforcement Learning Project

Install

This project requires Python 2.7 with the pygame library installed:

https://www.pygame.org/wiki/GettingStarted

Code

Open smartcab/agent.py and implement LearningAgent. Follow TODOs for further instructions.

Run

Make sure you are in the top-level project directory smartcab/ (that contains this README). Then run:

python smartcab/agent.py

OR:

python -m smartcab.agent

License

The contents of this repository are covered under the MIT License.