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


In this chapter, we went through different state of the art approaches in object detection such as R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, and others. Furthermore, we explored an approach given by Hierarchical Object Detection with Deep Reinforcement Learningby Bellver et. al. (2016). As per this approach we learnt how to create an MDP framework for object detection and hierarchically detect objects in a top-bottom exploration approach in minimal time steps. Object detection in an image is one application in computer vision. There are other domains such as object detection in videos, video tagging, and many more where reinforcement learning can create state of the art learning agents.

In the next chapter, we will learn how reinforcement learning can be applied in the domain of NLP (natural language processing).