Book Image

Reinforcement Learning with TensorFlow

By : Sayon Dutta
Book Image

Reinforcement Learning with TensorFlow

By: Sayon Dutta

Overview of this book

Reinforcement learning (RL) allows you to develop smart, quick and self-learning systems in your business surroundings. It's an effective method for training learning agents and solving a variety of problems in Artificial Intelligence - from games, self-driving cars and robots, to enterprise applications such as data center energy saving (cooling data centers) and smart warehousing solutions. The book covers major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. You'll also be introduced to the concept of reinforcement learning, its advantages and the reasons why it's gaining so much popularity. You'll explore MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, and temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have gained a firm understanding of what reinforcement learning is and understand how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.
Table of Contents (21 chapters)
Title Page
Packt Upsell

Chapter 12. Deep Reinforcement Learning in Ad Tech

So far in this unit of discussing reinforcement learning application research domains, we saw how reinforcement learning is disrupting the field of robotics, autonomous driving, financial portfolio management, and solving games of extremely high complexity, such as Go. Another important domain which is likely to be disrupted by reinforcement learning is advertisement technology.

Before getting into the details of the problem statement and it's solution based on reinforcement learning, let's understand the challenges, business models, and bidding strategies involved, which will work as a basic prerequisite in understanding the problem that we will try to solve using a reinforcement learning framework. The topics that we will be covering in this chapter are as follows:

  • Computational advertising challenges and bidding strategies

  • Real-time bidding by reinforcement learning in display advertising