In this section, you will learn about the creation of successful maze-solver agents using a goal-oriented objective function to guide the evolutionary process. This objective function is based on the estimation of the fitness score of the maze solver by measuring the distance between its final position and the maze exit after executing the 400 simulation steps. Thus, the objective function is goal-oriented and solely depends on the ultimate goal of the experiment: reaching the maze exit area.
In the next chapter, we will consider a different approach for solution search optimization, which is based on the Novelty Search (NS) optimization method. The NS optimization method is built around exploring new configurations of the solver agent during evolution and doesn't include proximity to the final goal (in this case, the...
