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

Summary

In this chapter, we discussed how neuroevolution can be used to train large ANNs with more than 4 million trainable parameters. You learned how to apply this learning method to create successful agents that are able to play classic Atari games by learning the game rules solely from observing the game screens. By completing the Atari game-playing experiment that was described in this chapter, you have learned about CNNs and how they can be used to map high-dimensional inputs, such as game screen observations, into the appropriate game actions. You now have a solid understanding of how CNNs can be used for value-function approximations in the deep RL method, which is guided by the deep neuroevolution algorithm.

With the knowledge that you've acquired from this chapter, you will be able to apply deep neuroevolution methods in domains with high-dimensional input data...