Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Other Books You May Enjoy

Final glue – loss functions and optimizers

The network which transforms input data into output is not enough to start training it. 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 of those pieces—the loss function and the method of tweaking a network's parameters by gradients—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 an nn.Module subclass. Usually, they accept two...