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

Real-time bidding by reinforcement learning in display advertising

Online displays are majorly served through real-time bidding where each impression of the display advertisement is auctioned in real time simultaneously when generated from a user visit. Placing a bid automatically, and in real time, is highly critical for advertisers to maximize their profits. Thus, a learning algorithm needs to be devised that can devise an optimal learning strategy in real time based on historical data, so that dynamic allocation of the budget takes place across different impressions according to immediate and future returns. Here, we will discuss formulating a bid-decision process in terms of a reinforcement learning framework published in Real-Time Bidding by Reinforcement Learning in Display Advertising by Cai et. al. 2017.

In this research by Cai et. al., the machine bidding in the context of display advertising is considered, where real-time bidding is a highly challenging task because, in the case...