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PyTorch 1.x Reinforcement Learning Cookbook

You're reading from   PyTorch 1.x Reinforcement Learning Cookbook Over 60 recipes to design, develop, and deploy self-learning AI models using Python

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781838551964
Length 340 pages
Edition 1st Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Reinforcement Learning and PyTorch FREE CHAPTER 2. Markov Decision Processes and Dynamic Programming 3. Monte Carlo Methods for Making Numerical Estimations 4. Temporal Difference and Q-Learning 5. Solving Multi-armed Bandit Problems 6. Scaling Up Learning with Function Approximation 7. Deep Q-Networks in Action 8. Implementing Policy Gradients and Policy Optimization 9. Capstone Project – Playing Flappy Bird with DQN 10. Other Books You May Enjoy

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Hands-On Reinforcement Learning with Python
Sudharsan Ravichandiran

ISBN: 978-1-78883-652-4

  • Understand the basics of reinforcement learning methods, algorithms, and elements
  • Train an agent to walk using OpenAI Gym and Tensorflow
  • Understand the Markov Decision Process, Bellman’s optimality, and TD learning
  • Solve multi-armed-bandit problems using various algorithms
  • Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
  • Build intelligent agents using the DRQN algorithm to play the Doom game
  • Teach agents to play the Lunar Lander game using DDPG
  • Train an agent to win a car racing game using dueling DQN

Python Reinforcement Learning Projects
Rajalingappaa Shanmugamani, Sean Saito, Et al

ISBN: 978-1-78899-161-2

  • Train and evaluate neural networks...
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