In this chapter, we discussed how neuroevolution can be used to train large ANNs with more than 4 million trainable parameters. You learned how to apply this learning method to create successful agents that are able to play classic Atari games by learning the game rules solely from observing the game screens. By completing the Atari game-playing experiment that was described in this chapter, you have learned about CNNs and how they can be used to map high-dimensional inputs, such as game screen observations, into the appropriate game actions. You now have a solid understanding of how CNNs can be used for value-function approximations in the deep RL method, which is guided by the deep neuroevolution algorithm.
With the knowledge that you've acquired from this chapter, you will be able to apply deep neuroevolution methods in domains with high-dimensional input data...
