In the last chapter, we explored NeuroEvolution of Augmenting Topologies (NEAT) to solve common problems we explored in previous chapters. In this chapter, we look at the evolution of learning itself. First, we use NEAT to develop an evolving agent that can solve problems typically associated with RL. Then, we look at more difficult RL problems and provide a NEAT solution for evolutionary learning. Finally, we finish the chapter by looking at how our understanding of learning itself needs to evolve, using a mental model called instinctual learning.
Reinforcement learning (RL) is a form of learning based on animal behavior and psychology that attempts to replicate how organisms learn through rewards. If you have ever trained a pet to do a trick using some form of reward, like a treat or praise, then you understand the premise. Many believe the basis for understanding high-level conscience and how we learn is modeled in RL.