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

This chapter provides a practical approach to building value-based, policy-based, and actor-critic algorithm-based reinforcement learning (RL) agents. It includes recipes for implementing value iteration-based learning agents and breaks down the implementation details of several foundational algorithms in RL into simple steps. The policy gradient-based agent and the actor-critic agent make use of the latest major version of TensorFlow 2.x to define the neural network policies.

The following recipes will be covered in this chapter:

  • Building stochastic environments for training RL agents
  • Building value-based (RL) 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...