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)
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SARSA algorithm

SARSA (the name is derived from the sequence state-action-reward-state-action) is a natural extension of TD(0) to the estimation of the Q function. Its standard formulation (which is sometimes called one-step SARSA, or SARSA(0), for the same reasons explained in the previous chapter) is based on a single next reward rt+1, which is obtained by executing the action at in the state st. The temporal difference computation is based on the following update rule:

The equation is equivalent to TD(0), and if the policy is chosen to be GLIE, it has been proven (in Singh S., Jaakkola T., Littman M. L., Szepesvári C., Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms, Machine Learning, 39/2000) that SARSA converges to an optimal policy with a probability of 1 when all couples (state, action) are experienced an infinite number of times. This means that if the policy is updated to be greedy with respect to the current value function induced...