In this book, we have already covered how the neuroevolution method can be applied to solve simple reinforcement learning (RL) tasks, such as single- and double-pole balancing in Chapter 4, Pole-Balancing Experiments. However, while the pole-balancing experiment is exciting and easy to conduct, it is pretty simple and operates with tiny artificial neural networks. In this chapter, we will discuss how to apply neuroevolution to reinforcement learning problems that require immense ANNs to approximate the value function of the RL algorithm.
The RL algorithm learns through trial and error. Almost all the variants of RL algorithms try to optimize the value function, which maps the current state of the system to the appropriate action that will be performed in the next time step. The most widely used classical version of the RL algorithm...
