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

The TD(0) algorithm

One of the problems with dynamic programming algorithms is the need for full knowledge of the environment in terms of states and transition probabilities. Unfortunately, there are many cases where these pieces of information are unknown before the direct experience. In particular, states can be discovered by letting the agent explore the environment, but transition probabilities require us to count the number of transitions to a certain state and this is often impossible. Moreover, an environment with absorbing states can prevent visiting many states if the agent has learned a good initial policy. For example, in a game, which can be described as an episodic MDP, the agent discovers the environment while learning how to move forward without ending in a negative absorbing state.

A general solution to these problems is provided by a different evaluation strategy, called Temporal Difference (TD) RL. In this case, we start with an empty value matrix and we let the...