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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

Product type Paperback

Published in Mar 2019

Publisher Packt

ISBN-13 9781789533583

Length 184 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Reinforcement Learning

Author (1):

Kaushik Balakrishnan

View More author details

Table of Contents (11) Chapters

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

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Training a DDPG agent to learn to drive

Most of the DDPG code is the same as we saw earlier in Chapter 5, Deep Deterministic Policy Gradients (DDPG); only the differences will be summarized here.

Coding ddpg.py

Our state dimension for TORCS is 29 and the action dimension is 3; these are set in ddpg.py as follows:

state_dim = 29
action_dim = 3
action_bound = 1.0

Coding AandC.py

The actor and critic file, AandC.py, also needs to be modified. In particular, the create_actor_network in the ActorNetwork class is edited to have two hidden layers with 400 and 300 neurons, respectively...

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Authors (1)

Balakrishnan

Balakrishnan

Kaushik Balakrishnan works for BMW in Silicon Valley, and applies reinforcement learning, machine learning, and computer vision to solve problems in autonomous driving. Previously, he also worked at Ford Motor Company and NASA Jet Propulsion Laboratory. His primary expertise is in machine learning, computer vision, and high-performance computing, and he has worked on several projects involving both research and industrial applications. He has also worked on numerical simulations of rocket landings on planetary surfaces, and for this he developed several high-fidelity models that run efficiently on supercomputers. He holds a PhD in aerospace engineering from the Georgia Institute of Technology in Atlanta, Georgia.

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