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You're reading from  Hands-On Reinforcement Learning with Python

Product typeBook
Published inJun 2018
Reading LevelIntermediate
PublisherPackt
ISBN-139781788836524
Edition1st Edition
Languages
Python
Tools
TensorFlowKeras
Concepts
Reinforcement Learning
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Author (1)
Sudharsan Ravichandiran
Sudharsan Ravichandiran
author image
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.
Read more about Sudharsan Ravichandiran

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Chapter 12

  1. DQN computes the Q value directly whereas Dueling DQN breaks down the Q value computation into value function and advantage function.
  2. Refer section Replay memory.
  3. When we use the same network for predicting target value and predicted value there will lot of divergence so we use separate target network.
  4. Refer section Replay memory.
  5. Refer section Dueling network.
  6. Dueling DQN breaks down the Q value computation into value function and advantage function whereas double DQN uses two Q function to avoid overestimation.
  7. Refer section Dueling network.
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Hands-On Reinforcement Learning with Python
Published in: Jun 2018Publisher: PacktISBN-13: 9781788836524
© 2018 Packt Publishing Limited All Rights Reserved

Author (1)

author image
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.
Read more about Sudharsan Ravichandiran

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