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 14. Deep Reinforcement Learning in NLP

Reinforcement learning in natural language processing (NLP) became a hot topic of research in the artificial intelligence community no more than a year ago. Most of the research publications catering to the use of reinforcement learning in NLP were published in the latter half of 2017.

The biggest reason behind the use of a reinforcement learning framework in any domain is the representation of the environment in the form of state, an exhaustive list of all possible actions in the environment, and a domain-specific reward function to achieve the goal through the most optimized path of actions. Thus, if a system has many possible actions but the correct set of actions is not given, and the objective highly depends on different options (actions) of the system then reinforcement learning framework can model the system better than existing supervised or unsupervised models.

Why use reinforcement learning in NLP ?

  • NLP-oriented systems, such as text...