Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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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 st as the expected discounted return obtained by the agent starting from st and following the stochastic policy :

In this case, we are assuming that, as the agent will follow , the state sa is more useful than sb if the expected return starting from sa is greater than the one obtained starting from sb. 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...