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

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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

Q-learning

This algorithm was proposed by Watkins (in Watkins C.I.C.H., Learning from delayed rewards, Ph.D. Thesis, University of Cambridge, 1989 and furtherly analyzed in Watkins C.I.C.H., Dayan P., Technical Note Q-Learning, Machine Learning 8, 1992) as a more efficient alternative to SARSA. The main feature of Q-learning is that the TD update rule is immediately greedy with respect to the Q(s_t₊₁, a) function (assuming that the agent received the reward r_t after performing the action at while in the state s_t):

The key idea is to compare the current Q(s_t, a_t) value with the maximum Q value achievable when the agent is in the successor state. Assuming , the previous equation can be transformed into a TD_error structure:

The first term is the current reward, the second is the discounted maximum reward that the agent can theoretically achieve using its current knowledge and the last one is the estimation of the Q function. As the policy must be GLIE, the convergence...