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

Double-pole balancing experiment

This experiment uses a version of the double-pole balancing problem that assumes full knowledge of the current system state, including the angular velocities of the poles and the velocity of the cart. The criteria of success in this experiment are to keep both poles balanced for 100,000 steps, or approximately 33 minutes of simulated time. The pole is considered balanced when it stays within degrees of vertical, while the cart remains within meters of the track's center.

Hyperparameter selection

Compared to the previous experiment described in this chapter, double-pole balancing is much harder to solve due to its complex motion dynamics. Thus, the search space for a successful control...