$Automated \ Vehicle’s \ behavior \ decision \ making\  using \ deep \ reinforcement \ learning \ and \ high-fidelity \ simulation \ environment$
https://arxiv.org/pdf/1804.06264.pdf


University of Michigan - Ann Arbor

A good way to understand reinforcement learning would be to think about how autonomous vehicles work. Instead of programming all of the rules it should follow (such as road signs, pedestrians, right of way, etc), it is much more easier and efficient to implement a reward/penalty system where the car learns all of the rules that it is supposed to abide by. Furthermore, it would be unreasonable to program individual rules because accidents can happen in unpredictable ways and rules/regulations are always changing and varies in different parts of the world. So, it is much better for autonomous cars to be always learning better ways to make decisions.

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