Book Image

Hands-On Neuroevolution with Python

By : Iaroslav Omelianenko
Book Image

Hands-On Neuroevolution with Python

By: Iaroslav Omelianenko

Overview of this book

Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods
4
Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
9
Section 3: Advanced Neuroevolution Methods
14
Section 4: Discussion and Concluding Remarks

The fitness function with the novelty score

Now we have defined the basic principles behind the NS method, we need to find a way to integrate it into the definition of the fitness function that will be used to guide the neuroevolution process. In other words, we need to define the novelty metric that can capture the amount of novelty that is introduced by a particular solver agent during the evolutionary process. There are several characteristics that can be used as novelty metrics for a solver agent:

  • The novelty of the solver genotype structure—the structural novelty
  • The stepping stones found in the search space of the solution—the behavioral novelty

Our primary interest in this chapter is to create a successful maze navigator agent. To successfully navigate through the maze, the agent must pay equal attention to most places in the maze. Such behavior can be achieved...