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

Policy iteration

In this section, we are going to analyze a strategy to find an optimal policy based on complete knowledge of the environment (in terms of transition probability and expected returns). The first step is to define a method that can be employed to build a greedy policy. Let's suppose we're working with a finite MDP and a generic policy ; we can define the intrinsic value of a state s_t as the expected discounted return obtained by the agent starting from s_t and following the stochastic policy :

In this case, we are assuming that, as the agent will follow , the state s_a is more useful than s_b if the expected return starting from s_a is greater than the one obtained starting from s_b. Unfortunately, trying to directly find the value of each state using the previous definition is almost impossible when . However, this is a problem that can be solved using dynamic programming (for further information, please refer to R. A. Howard, Dynamic Programming and...