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Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

You're reading from  Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

Product type Book
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Pages 512 pages
Edition 2nd Edition
Languages
Author (1):
Rowel Atienza Rowel Atienza
Profile icon Rowel Atienza

Table of Contents (16) Chapters

Preface 1. Introducing Advanced Deep Learning with Keras 2. Deep Neural Networks 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods 11. Object Detection 12. Semantic Segmentation 13. Unsupervised Learning Using Mutual Information 14. Other Books You May Enjoy
15. Index

Deep Reinforcement Learning

Reinforcement Learning (RL) is a framework that is used by an agent for decision making. The agent is not necessarily a software entity, such as you might see in video games. Instead, it could be embodied in hardware such as a robot or an autonomous car. An embodied agent is probably the best way to fully appreciate and utilize RL, since a physical entity interacts with the real world and receives responses.

The agent is situated within an environment. The environment has a state that can be partially or fully observable. The agent has a set of actions that it can use to interact with its environment. The result of an action transitions the environment to a new state. A corresponding scalar reward is received after executing an action.

The goal of the agent is to maximize the accumulated future reward by learning a policy that will decide which action to take given a state.

RL has a strong similarity to human psychology. Humans learn...

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