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 3: Implementing Advanced RL Algorithms

This chapter provides short and crisp recipes to implement advanced Reinforcement Learning (RL) algorithms and agents from scratch using TensorFlow 2.x. It includes recipes to build Deep-Q-Networks (DQN), Double and Dueling Deep Q-Networks (DDQN, DDDQN), Deep Recurrent Q-Networks (DRQN), Asynchronous Advantage Actor-Critic (A3C), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradients (DDPG).

The following recipes are discussed in this chapter:

  • 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...