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Hands-On Reinforcement Learning with Python

You're reading from Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788836524

Length 318 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (16) Chapters

Preface

1. Introduction to Reinforcement Learning

2. Getting Started with OpenAI and TensorFlow FREE CHAPTER

3. The Markov Decision Process and Dynamic Programming

4. Gaming with Monte Carlo Methods

5. Temporal Difference Learning

6. Multi-Armed Bandit Problem

7. Deep Learning Fundamentals

8. Atari Games with Deep Q Network

9. Playing Doom with a Deep Recurrent Q Network

10. The Asynchronous Advantage Actor Critic Network

11. Policy Gradients and Optimization

12. Capstone Project – Car Racing Using DQN

13. Recent Advancements and Next Steps

14. Assessments

15. Other Books You May Enjoy

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DARQN

We have improved our DQN architecture by adding a recurrent layer, which captures temporal dependency, and we called it DRQN. Do you think we can improve our DRQN architecture further? Yes. We can further improve our DRQN architecture by adding the attention layer on top of the convolutional layer. So, what is the function of the attention layer? Attention implies the literal meaning of the word. Attention mechanisms are widely used in image captioning, object detection, and so on. Consider the task of neural networks captioning the image; to understand what is in the image, the network has to give attention to the specific object in the image for generating the caption.

Similarly, when we add the attention layer to our DRQN, we can select and pay attention to small regions of the image, and ultimately this reduces the number of parameters in the network and also reduces...

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

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