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Hands-On Neuroevolution with Python

Hands-On Neuroevolution with Python

By : Omelianenko
3 (1)
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Hands-On Neuroevolution with Python

Hands-On Neuroevolution with Python

3 (1)
By: 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)
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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

Running the experiment with a simple maze configuration

We start our experiments related to the creation of the successful maze navigation agent with a simple maze configuration. The simple maze configuration, while having the deceptive local optima cul-de-sacs discussed earlier, has a relatively straightforward path from the start point to the exit point.

The following diagram represents the maze configuration used for this experiment:

The simple maze configuration

The maze in the diagram has two specific positions marked with filled circles. The top-left circle denotes the starting position of the maze navigator agent. The bottom-right circle marks the exact location of the maze exit that needs to be found by the maze solver. The maze solver is required to reach the vicinity of the maze exit point denoted by the specific exit range area around it in order to complete the task...

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