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 learned how reinforcement learning can disrupt the domain of NLP. We studied the reasons behind the use of reinforcement learning in NLP. We covered two big application domains in NLP, that is, text summarization and question answering, and understood the basics of how a reinforcement learning framework was implemented in the existing models to obtain state-of-the-art results. There are other application domains in NLP where reinforcement learning has been implemented, such as dialog generation and machine translation (discussing them is out of the scope of this book).

This brings us to the end of this amazing journey of deep reinforcement learning. We started with the basics by understanding the concepts, then implemented those concepts using TensorFlow and OpenAI Gym, and went through cool research areas where deep reinforcement learning is being implemented at the core level. I hope the journey was interesting and we were able to build the best foundation possible...