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

Hierarchical object detection with deep reinforcement learning

In this section, we will try to understand how deep reinforcement learning can be applied for hierarchical object detection as per the framework suggested in Hierarchical Object Detection with Deep Reinforcement Learning by Bellver et. al. (2016)( This experiment showcases a method to perform hierarchical object detection in images using deep reinforcement learning with the main focus on important parts of the image carrying richer information. The objective here was to train a deep reinforcement learning agent to which an image window is given and the image gets further segregated into five smaller windows and the agent is successfully able to focus its attention on one of the smaller windows.

Now let's consider how we humans look at an image. We always extract information in a sequential manner to understand the content of the image:

  • First, we focus on the most important part of the image...