Preface

Deep reinforcement learning enables the building of 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 that is used to develop and train deep neural networks (DNNs).

The book begins with an introduction to the fundamentals of deep reinforcement learning and the latest major version of TensorFlow 2.x. You'll then cover OpenAI Gym, model-based RL, and model-free RL, and learn how to develop basic agents. Moving on, you will discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, deep recurrent Q-networks, and the soft actor-critic algorithm to train your RL agents. You'll also explore reinforcement learning in the real world by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Lastly, you will find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps for the web, mobile, and other platforms using TensorFlow 2.x.

By the end of this cookbook, you will have gained a solid understanding of deep reinforcement learning algorithms with the help of easy-to-follow and concise implementations from scratch using TensorFlow 2.x.