Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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Index

The final glue – loss functions and optimizers

The network that transforms input data into output is not the only thing we need for training. We need to define our learning objective, which is to have a function that accepts two arguments—the network's output and the desired output. Its responsibility is to return to us a single number—how close the network's prediction is from the desired result. This function is called the loss function, and its output is the loss value. Using the loss value, we calculate gradients of network parameters and adjust them to decrease this loss value, which pushes our model to better results in the future. Both the loss function and the method of tweaking a network's parameters by gradient are so common and exist in so many forms that both of them form a significant part of the PyTorch library. Let's start with loss functions.

Loss functions

Loss functions reside in the nn package and are implemented as...