Palanisamy TensorFlow 2 Reinforcement Learning Cookbook

Over 50 recipes to help you build, train, and deploy learning agents for real-world applications
1. Auflage 2024
ISBN: 978-1-83898-599-8
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 472 Seiten

Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learningKey Features - Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services
- Explore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic method
- Customize and build RL-based applications for performing real-world tasks
Book DescriptionWith deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.What you will learn - Build deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras API
- Implement state-of-the-art deep reinforcement learning algorithms using minimal code
- Build, train, and package deep RL agents for cryptocurrency and stock trading
- Deploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud services
- Speed up agent development using distributed DNN model training
- Explore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)
Who this book is forThe book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.

Autoren/Hrsg.

Palanisamy, Palanisamy

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

Table of Contents - Developing building blocks for Deep RL using TensorFlow 2.x
- Implementing value-based, policy gradients and actor-critic Deep RL algorithms
- Implementing Advanced Deep RL algorithms
- RL in real-world: Building intelligent trading agents
- RL in Real-World: Building Stock Trading Agents
- RL in real-world: Building intelligent agents to complete your ToDos
- Deploying Deep RL Agents to the Cloud

- Building cross-platform (web, desktop, mobile) Deep-RL Apps using TensorFlow 2.x

- Distributed training and automated production deployment pipeline for Deep RL Apps

Über Autor(innen)

P Palanisamy :

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.