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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

Implementing Policy Gradients and Policy Optimization

In this chapter, we will focus on policy gradient methods as one of the most popular reinforcement learning techniques over recent years. We will start with implementing the fundamental REINFORCE algorithm and will proceed with an improvement algorithm baseline. We will also implement a more powerful algorithm, actor-critic, and its variations, and apply it to solve the CartPole and Cliff Walking problems. We will also experience an environment with continuous action space and resort to Gaussian distribution to solve it. By way of a fun section at the end, we will train an agent based on the cross-entropy method to play the CartPole game.

The following recipes will be covered in this chapter:

  • Implementing the REINFORCE algorithm
  • Developing the REINFORCE algorithm with baseline
  • Implementing the actor-critic algorithm
  • Solving...
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