Skip to content

Examines the role of states, actions, rewards, models, and policy in a simplified world that characterizes driving in a modern urban city.

Notifications You must be signed in to change notification settings

kvn219/smartcab

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

17 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Training a Smartcab with Reinforcement Learning

Udacity Project 4

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 smartcab/random_agent.py

python smartcab/informed_agent.py

python smartcab/q_agent.py

python smartcab/parameter_search.py

About

Examines the role of states, actions, rewards, models, and policy in a simplified world that characterizes driving in a modern urban city.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published