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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

Deploying the model and playing the game

Now that we've trained the DQN model, let's apply it to play the Flappy Bird game.

Playing the game with the trained model is simple. We will just take the action associated with the highest value in each step. We will play a few episodes to see how it performs. Don’t forget to preprocess the raw screen image and construct the state.

How to do it...

We test the DQN model on new episodes as follows:

  1. We first load the final model:
>>> model = torch.load("{}/final".format(saved_path))
  1. We run 100 episodes, and we perform the following for each episode:
>>> n_episode = 100
>>> for episode in range(n_episode):

... env = FlappyBird...
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