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Hands-On Reinforcement Learning with Python

You're reading from   Hands-On Reinforcement Learning with Python Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788836524
Length 318 pages
Edition 1st 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 (16) Chapters Close

Preface 1. Introduction to Reinforcement Learning FREE CHAPTER 2. Getting Started with OpenAI and TensorFlow 3. The Markov Decision Process and Dynamic Programming 4. Gaming with Monte Carlo Methods 5. Temporal Difference Learning 6. Multi-Armed Bandit Problem 7. Deep Learning Fundamentals 8. Atari Games with Deep Q Network 9. Playing Doom with a Deep Recurrent Q Network 10. The Asynchronous Advantage Actor Critic Network 11. Policy Gradients and Optimization 12. Capstone Project – Car Racing Using DQN 13. Recent Advancements and Next Steps 14. Assessments 15. Other Books You May Enjoy

Training the network

Now, we will see how to train the network.

First, we define the DQN class and initialize all variables in the __init__ method:

class DQN(object):
def __init__(self, state_size,
action_size,
session,
summary_writer = None,
exploration_period = 1000,
minibatch_size = 32,
discount_factor = 0.99,
experience_replay_buffer = 10000,
target_qnet_update_frequency = 10000,
initial_exploration_epsilon = 1.0,
final_exploration_epsilon = 0.05,
reward_clipping = -1,
):

Initialize all variables:

   
self.state_size = state_size
self.action_size = action_size


self.session = session
...
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