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Reinforcement Learning with TensorFlow

You're reading from   Reinforcement Learning with TensorFlow A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788835725
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Sayon Dutta Sayon Dutta
Author Profile Icon Sayon Dutta
Sayon Dutta
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Toc

Table of Contents (17) Chapters Close

Preface 1. Deep Learning – Architectures and Frameworks FREE CHAPTER 2. Training Reinforcement Learning Agents Using OpenAI Gym 3. Markov Decision Process 4. Policy Gradients 5. Q-Learning and Deep Q-Networks 6. Asynchronous Methods 7. Robo Everything – Real Strategy Gaming 8. AlphaGo – Reinforcement Learning at Its Best 9. Reinforcement Learning in Autonomous Driving 10. Financial Portfolio Management 11. Reinforcement Learning in Robotics 12. Deep Reinforcement Learning in Ad Tech 13. Reinforcement Learning in Image Processing 14. Deep Reinforcement Learning in NLP 15. Further topics in Reinforcement Learning 16. Other Books You May Enjoy

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...

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