Challenging On Car Racing Problem from OpenAI gym
  This project challenges the car racing problem from OpenAI gym environment.
The problem is very challenging since it requires computer to finish the
continuous control task by learning from pixels. To tackle this challenging
problem, we explored two approaches including evolutionary algorithm based
genetic multi-layer perceptron and double deep Q-learning network. The result
shows that the genetic multi-layer perceptron can converge fast but when
training many episodes, double deep Q-learning can get better score. We analyze
the result and draw a conclusion that for limited hardware resources, using
genetic multi-layer perceptron sometimes can be more efficient.
