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TensorFlow Reinforcement Learning Quick Start Guide

You're reading from   TensorFlow Reinforcement Learning Quick Start Guide Get up and running with training and deploying intelligent, self-learning agents using Python

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789533583
Length 184 pages
Edition 1st Edition
Languages
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Author (1):
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Kaushik Balakrishnan Kaushik Balakrishnan
Author Profile Icon Kaushik Balakrishnan
Kaushik Balakrishnan
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Table of Contents (11) Chapters Close

Preface 1. Up and Running with Reinforcement Learning 2. Temporal Difference, SARSA, and Q-Learning FREE CHAPTER 3. Deep Q-Network 4. Double DQN, Dueling Architectures, and Rainbow 5. Deep Deterministic Policy Gradient 6. Asynchronous Methods - A3C and A2C 7. Trust Region Policy Optimization and Proximal Policy Optimization 8. Deep RL Applied to Autonomous Driving 9. Assessment 10. Other Books You May Enjoy

Evaluating the performance

The PPO agent is trained by the following command:

python train_test.py

Once the training is complete, we can test the agent by setting the following:

train_test = 1

Then, we will repeat python train_test.py again. On visualizing the agent, we can observe that the car first moves backward to climb the left mountain. Then it goes full throttle and picks up enough momentum to drive past the steep slope of the right mountain with the flag on top. So, the PPO agent has learned to drive out of the mountain valley successfully.

Full throttle

Note that we had to navigate backward first and then step on the throttle in order to have sufficient momentum to escape gravity and successfully drive out of the...

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