Book description
Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning
Key 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 Description
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 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 for
The 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.
Table of contents
- TensorFlow 2 Reinforcement Learning Cookbook
- Why subscribe?
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
-
Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x
- Technical requirements
- Building an environment and reward mechanism for training RL agents
- Implementing neural network-based RL policies for discrete action spaces and decision-making problems
- Implementing neural network-based RL policies for continuous action spaces and continuous-control problems
- Working with OpenAI Gym for RL training environments
- Building a neural agent
- Building a neural evolutionary agent
-
Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms
- Technical requirements
- Building stochastic environments for training RL agents
- Building value-based reinforcement learning agent algorithms
- Implementing temporal difference learning
- Building Monte Carlo prediction and control algorithms for RL
- Implementing the SARSA algorithm and an RL agent
- Building a Q-learning agent
- Implementing policy gradients
- Implementing actor-critic RL algorithms
-
Chapter 3: Implementing Advanced RL Algorithms
- Technical requirements
- Implementing the Deep Q-Learning algorithm, DQN, and Double-DQN agent
- Implementing the Dueling DQN agent
- Implementing the Dueling Double DQN algorithm and DDDQN agent
- Implementing the Deep Recurrent Q-Learning algorithm and DRQN agent
- Implementing the Asynchronous Advantage Actor-Critic algorithm and A3C agent
- Implementing the Proximal Policy Optimization algorithm and PPO agent
- Implementing the Deep Deterministic Policy Gradient algorithm and DDPG agent
- Chapter 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents
- Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents
-
Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos
- Technical requirements
- Building learning environments for real-world RL
- Building an RL Agent to complete tasks on the web – Call to Action
- Building a visual auto-login bot
- Training an RL Agent to automate flight booking for your travel
- Training an RL Agent to manage your emails
- Training an RL Agent to automate your social media account management
-
Chapter 7: Deploying Deep RL Agents to the Cloud
- Technical requirements
- Implementing the RL agent’s runtime components
- Building RL environment simulators as a service
- Training RL agents using a remote simulator service
- Testing/evaluating RL agents
- Packaging RL agents for deployment – a trading bot
- Deploying RL agents to the cloud – a trading Bot-as-a-Service
-
Chapter 8: Distributed Training for Accelerated Development of Deep RL Agents
- Technical requirements
- Distributed deep learning models using TensorFlow 2.x – Multi-GPU training
- Scaling up and out – Multi-machine, multi-GPU training
- Training Deep RL agents at scale – Multi-GPU PPO agent
- Building blocks for distributed Deep Reinforcement Learning for accelerated training
- Large-scale Deep RL agent training using Ray, Tune, and RLLib
- Chapter 9: Deploying Deep RL Agents on Multiple Platforms
- Other Books You May Enjoy
Product information
- Title: TensorFlow 2 Reinforcement Learning Cookbook
- Author(s):
- Release date: January 2021
- Publisher(s): Packt Publishing
- ISBN: 9781838982546
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