You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

View More author details

Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Advanced Policy Estimation Algorithms

In this chapter, we'll complete our exploration of the world of Reinforcement Learning (RL), focusing our attention on complex algorithms that can be employed to solve difficult problems. The topic of RL is extremely large, and we couldn't cover it in its entirety even if we dedicated an entire book to it; this chapter is instead based on many practical examples that you can use as a basis to work on more complex scenarios.

The topics that will be discussed in this chapter are:

The TD() algorithm
Actor-Critic TD(0)
SARSA
Q-learning, including a simple visual input and a neural network
Direct policy search through policy gradient

We can now start analyzing the natural extension of TD(0) algorithm, which helps take into account a longer sequence of transitions, obtaining a more accurate estimation of the value function.