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

Text summarization

Text summarization is the process of automatically generating summarized text of the document test fed as an input by retaining the important information of the document. Text summarization condenses a big set of information in a concise manner; therefore, summaries play an important role in applications related to news/articles, text search, and report generation.

There are two types of summarization algorithms:

  • Extractive summarization: Creates summaries by copying parts of the text from the input text
  • Abstractive summarization: Generates new text by rephrasing the text or using new words that were not in the input text

The attention-based encoder decoder model created for machine translation (Bahdanau et al., 2014)is a sequence-to-sequence model and was able to generate abstractive summaries with good performance by achieving good ROUGE score (seeAppendix A, Further topics in Reinforcement Learning). The performance was good on short input sequences and it deteriorated...