In this chapter, you have learned about a class of planning and control problems that use goal-oriented fitness functions that have a deceptive definition landscape. In this landscape, there are multiple traps created by the local optima areas of the fitness function that mislead the solution search process, which is based only on the fitness score calculated as a derivative of the distance from the agent to the goal. You have learned that the conventional goal-oriented fitness function can help the search process to create a successful maze navigator agent for a simple maze configuration, but failed with a more complex maze due to the local optima traps.
We presented a useful visualization method that allowed us to visualize the final positions of all evaluated agents on the maze map. With this visualization, you can make assumptions about the performance of the evolutionary...
