Book Image

TensorFlow 2 Reinforcement Learning Cookbook

By : Palanisamy P

Book Image

TensorFlow 2 Reinforcement Learning Cookbook

By: Palanisamy P

Overview of this book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x

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

Free Chapter

Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms

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

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 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents

Technical requirements

Building a Bitcoin trading RL platform using real market data

Building an Ethereum trading RL platform using price charts

Building an advanced cryptocurrency trading platform for RL agents

Training a cryptocurrency trading bot using RL

Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents

Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents

Technical requirements

Building a stock market trading RL platform using real stock exchange data

Building a stock market trading RL platform using price charts

Building an advanced stock trading RL platform to train agents to mimic professional traders

Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

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

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

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

Chapter 9: Deploying Deep RL Agents on Multiple Platforms

Technical requirements

Packaging Deep RL agents for mobile and IoT devices using TensorFlow Lite

Deploying RL agents on mobile devices

Packaging Deep RL agents for the web and Node.js using TensorFlow.js

Deploying a Deep RL agent as a service

Packaging Deep RL agents for cross-platform deployment

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Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781838982546_ColorImages.pdf.