Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Reinforcement Learning Algorithms with Python

You're reading from   Reinforcement Learning Algorithms with Python Learn, understand, and develop smart algorithms for addressing AI challenges

Arrow left icon
Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789131116
Length 366 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Andrea Lonza Andrea Lonza
Author Profile Icon Andrea Lonza
Andrea Lonza
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Algorithms and Environments FREE CHAPTER
2. The Landscape of Reinforcement Learning 3. Implementing RL Cycle and OpenAI Gym 4. Solving Problems with Dynamic Programming 5. Section 2: Model-Free RL Algorithms
6. Q-Learning and SARSA Applications 7. Deep Q-Network 8. Learning Stochastic and PG Optimization 9. TRPO and PPO Implementation 10. DDPG and TD3 Applications 11. Section 3: Beyond Model-Free Algorithms and Improvements
12. Model-Based RL 13. Imitation Learning with the DAgger Algorithm 14. Understanding Black-Box Optimization Algorithms 15. Developing the ESBAS Algorithm 16. Practical Implementation for Resolving RL Challenges 17. Assessments
18. Other Books You May Enjoy

The Landscape of Reinforcement Learning

Humans and animals learn through a process of trial and error. This process is based on our reward mechanisms that provide a response to our behaviors. The goal of this process is to, through multiple repetitions, incentivize the repetition of actions which trigger positive responses, and disincentivize the repetition of actions which trigger negative ones. Through the trial and error mechanism, we learn to interact with the people and world around us, and pursue complex, meaningful goals, rather than immediate gratification.

Learning through interaction and experience is essential. Imagine having to learn to play football by only looking at other people playing it. If you took to the field to play a football match based on this learning experience, you would probably perform incredibly poorly.

This was demonstrated throughout the mid-20th...

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
Banner background image