Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Discussing Q-learning

The key difference between policy optimization and Q-learning is the fact that in the latter, we are not directly optimizing the policy. Instead, we optimize a value function. What is a value function? We have already learned that RL is all about an agent learning to gain the maximum overall rewards while traversing a trajectory of states and actions. A value function is a function of a given state the agent is currently at, and this function outputs the expected sum of rewards the agent will receive by the end of the current episode.

In Q-learning, we optimize a specific type of value function, known as the action-value function, which depends on both the current state and the action. At a given state, S, the action-value function determines the long-term rewards (rewards until the end of the episode) the agent will receive for taking action a. This function is usually expressed as Q(S, a), and hence is also called the Q-function. The action value is also...