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.
Table of Contents (11 chapters)

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

This chapter provides a practical and concrete description of the fundamentals of Deep Reinforcement Learning (Deep RL) filled with recipes for implementing the building blocks using the latest major version of TensorFlow 2.x. It includes recipes for getting started with RL environments, OpenAI Gym, developing neural network-based agents, and evolutionary neural agents for addressing applications with both discrete and continuous value spaces for Deep RL.

The following recipes are discussed in this chapter:

  • 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