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TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build,train,and deploy learning agents for real-world applications
by 作者: Praveen Palanisamy
Publisher Finelybook 出版社: Packt Publishing (15 Jan. 2021)
Language 语言: English
pages 页数: 472 pages
ISBN-10 书号: 183898254X
ISBN-13 书号: 9781838982546
Book Description
Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning
With 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 作者: 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 作者: 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)
