Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Reinforcement Learning Algorithms with Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Reinforcement Learning Algorithms with Python

Reinforcement Learning Algorithms with Python

By : Lonza
3 (3)
close
close
Reinforcement Learning Algorithms with Python

Reinforcement Learning Algorithms with Python

3 (3)
By: Lonza

Overview of this book

Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents. Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS. By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.
Table of Contents (19 chapters)
close
close
Free Chapter
1
Section 1: Algorithms and Environments
5
Section 2: Model-Free RL Algorithms
11
Section 3: Beyond Model-Free Algorithms and Improvements
17
Assessments

Deep Q-Network

So far, we've approached and developed reinforcement learning algorithms that learn about a value function, V, for each state, or an action-value function, Q, for each action-state pair. These methods involve storing and updating each value separately in a table (or an array). These approaches do not scale because, for a large number of states and actions, the table's dimensions increase exponentially and can easily exceed the available memory capacity.

In this chapter, we will introduce the use of function approximation in reinforcement learning algorithms to overcome this problem. In particular, we will focus on deep neural networks that are applied to Q-learning. In the first part of this chapter, we'll explain how to extend Q-learning with function approximation to store Q values, and we'll explore some major difficulties that we may face...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY