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Deep Reinforcement Learning with Python

You're reading from   Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781839210686
Length 760 pages
Edition 2nd Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Toc

Table of Contents (22) Chapters Close

Preface 1. Fundamentals of Reinforcement Learning 2. A Guide to the Gym Toolkit FREE CHAPTER 3. The Bellman Equation and Dynamic Programming 4. Monte Carlo Methods 5. Understanding Temporal Difference Learning 6. Case Study – The MAB Problem 7. Deep Learning Foundations 8. A Primer on TensorFlow 9. Deep Q Network and Its Variants 10. Policy Gradient Method 11. Actor-Critic Methods – A2C and A3C 12. Learning DDPG, TD3, and SAC 13. TRPO, PPO, and ACKTR Methods 14. Distributional Reinforcement Learning 15. Imitation Learning and Inverse RL 16. Deep Reinforcement Learning with Stable Baselines 17. Reinforcement Learning Frontiers 18. Other Books You May Enjoy
19. Index
Appendix 1 – Reinforcement Learning Algorithms 1. Appendix 2 – Assessments

Playing Atari games with a DQN and its variants

Now, let's learn how to create a DQN to play Atari games with Stable Baselines. First, let's import the necessary modules:

from stable_baselines import DQN

Since we are dealing with Atari games, we can use a convolutional neural network instead of a vanilla neural network. So, we use CnnPolicy:

from stable_baselines.deepq.policies import CnnPolicy

We learned that we preprocess the game screen before feeding it to the agent. With Stable Baselines, we don't have to preprocess manually; instead, we can make use of the make_atari module, which takes care of preprocessing the game screen:

from stable_baselines.common.atari_wrappers import make_atari

Now, let's create an Atari game environment. Let's create the Ice Hockey game environment:

env = make_atari('IceHockeyNoFrameskip-v4')

Instantiate the agent:

agent = DQN(CnnPolicy, env, verbose=1)

Train the agent...

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