Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Genetic Algorithms with Python

You're reading from   Hands-On Genetic Algorithms with Python Applying genetic algorithms to solve real-world deep learning and artificial intelligence problems

Arrow left icon
Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838557744
Length 346 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Eyal Wirsansky Eyal Wirsansky
Author Profile Icon Eyal Wirsansky
Eyal Wirsansky
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics of Genetic Algorithms
2. An Introduction to Genetic Algorithms FREE CHAPTER 3. Understanding the Key Components of Genetic Algorithms 4. Section 2: Solving Problems with Genetic Algorithms
5. Using the DEAP Framework 6. Combinatorial Optimization 7. Constraint Satisfaction 8. Optimizing Continuous Functions 9. Section 3: Artificial Intelligence Applications of Genetic Algorithms
10. Enhancing Machine Learning Models Using Feature Selection 11. Hyperparameter Tuning of Machine Learning Models 12. Architecture Optimization of Deep Learning Networks 13. Reinforcement Learning with Genetic Algorithms 14. Section 4: Related Technologies
15. Genetic Image Reconstruction 16. Other Evolutionary and Bio-Inspired Computation Techniques 17. Other Books You May Enjoy

Solving the MountainCar environment

The MountainCar-v0 environment simulates a car in a one-dimensional track, situated between two hills. The simulation starts with the car placed between the hills, as shown in the following rendered output:

MountainCar simulation—starting point

The goal is to get the car to climb up the taller hill—the one on the right—and ultimately hit the flag:

MountainCar simulation—car climbing the hill on the right

The simulation is set up with the car's engine being too weak to directly climb the taller hill. The only way to reach the goal is to drive the car back and forth until enough momentum is built for climbing. Climbing the left hill can help to achieve this goal as reaching the left peak will bounce the car back to the right, as shown in the following screenshot:

MountainCar simulation—car bouncing off...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime