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

TD() algorithm

In the previous chapter, we introduced the temporal difference strategy, and we discussed a simple example called TD(0). In the case of TD(0), the discounted reward is approximated by using a one-step backup.

Hence, if the agent performs an action at in the state st, and the transition to the state st+1 is observed, the approximation becomes the following:

If the task is episodic (as in many real-life scenarios) and has T(ei) steps, the complete backup for the episode ei is as follows:

The previous expression ends when the MDP process reaches an absorbing state; therefore, Rt is the actual value of the discounted reward. The difference between TD(0) and this choice is clear: in the first case, we can update the value function after each transition, whereas with a complete backup, we need to wait for the end of the episode. We can say that this method (which is called Monte Carlo, because it's based on the idea of averaging the overall...