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TensorFlow Reinforcement Learning Quick Start Guide

You're reading from   TensorFlow Reinforcement Learning Quick Start Guide Get up and running with training and deploying intelligent, self-learning agents using Python

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789533583
Length 184 pages
Edition 1st Edition
Languages
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Author (1):
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Kaushik Balakrishnan Kaushik Balakrishnan
Author Profile Icon Kaushik Balakrishnan
Kaushik Balakrishnan
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Table of Contents (11) Chapters Close

Preface 1. Up and Running with Reinforcement Learning 2. Temporal Difference, SARSA, and Q-Learning FREE CHAPTER 3. Deep Q-Network 4. Double DQN, Dueling Architectures, and Rainbow 5. Deep Deterministic Policy Gradient 6. Asynchronous Methods - A3C and A2C 7. Trust Region Policy Optimization and Proximal Policy Optimization 8. Deep RL Applied to Autonomous Driving 9. Assessment 10. Other Books You May Enjoy

Understanding Rainbow networks

We will now move on to Rainbow networks, which is a confluence of several different DQN improvements. Since the original DQN paper, several different improvements were proposed with notable success. This motivated DeepMind to combine several different improvements into an integrated agent, which they refer to as the Rainbow DQN. Specifically, six different DQN improvements are combined into one integrated Rainbow DQN agent. These six improvements are summarized as follows:

  • DDQN
  • Dueling network architecture
  • Prioritized experience replay
  • Multi-step learning
  • Distributional RL
  • Noisy nets

DQN improvements

We have already seen DDQN and dueling network architectures and have coded them in TensorFlow...

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