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 question answering

Question answering is the task where a document context is provided along with a question whose answer is present within the given document context. Existing models for question answering used to optimize the cross-entropy loss, which used to encourage the exact answers and penalize other probable answers that are equally accurate as the exact answer. These existing question answering models (state of the art dynamic coattention network by Xiong et. al. 2017) are trained to output exact answer spans from the document context for the question asked. The start and end position of the actual ground truth answer is used as the target for this supervised learning approach. Thus, this supervised model uses cross-entropy loss over both the positions and the objective is to minimize this overall loss over both the positions.

As we can see, the optimization is done by using the positions and evaluation is done by using the textual content of the answer. Thus, there is a disconnect...