In this chapter, you learned about the Novelty Search optimization method and how it can be used to guide the neuroevolution process in deceptive problem space environments, such as maze navigation. We conducted the same maze navigation experiments as in the previous chapter. After that, we compared the results we obtained to determine if the NS method has advantages over the goal-oriented optimization method introduced in the previous chapter.
You got the practical experience of writing source code using Python and experimented with tuning the important hyperparameters of the NEAT algorithm. Also, we introduced a new visualization method, allowing you to see the path of the agent through the maze. With this method, you can easily compare how different agents are trying to solve the maze navigation problem and whether the path through the maze that was found is optimal...
