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Applied reinforcement learning to design a scheme to automatically learn an optimal driving strategy based on rewards and penalties.

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zyaj/smartcab

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Project: Train a Smartcab How to Drive

Install

This project requires Python 2.7 with the pygame library installed

Code

Main 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.

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Applied reinforcement learning to design a scheme to automatically learn an optimal driving strategy based on rewards and penalties.

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