DQN refers to an important class of reinforcement learning, called value learning. Here, we use a deep neural network to learn the optimal Q-value function. For every iteration, the network approximates Q-value and evaluates them against the Bellman equation in order to measure the agent accuracy. Q-value is supposed to be optimized while the agent makes movements in the world. So, how we configure the Q-learning process is important. In this recipe, we will configure DQN for a Malmo mission and train the agent to achieve the task.
Java Deep Learning Cookbook
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Java Deep Learning Cookbook
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Overview of this book
Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently.
This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results.
By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java.
Table of Contents (14 chapters)
Preface
Free Chapter
Introduction to Deep Learning in Java
Data Extraction, Transformation, and Loading
Building Deep Neural Networks for Binary Classification
Building Convolutional Neural Networks
Implementing Natural Language Processing
Constructing an LSTM Network for Time Series
Constructing an LSTM Neural Network for Sequence Classification
Performing Anomaly Detection on Unsupervised Data
Using RL4J for Reinforcement Learning
Developing Applications in a Distributed Environment
Applying Transfer Learning to Network Models
Benchmarking and Neural Network Optimization
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