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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

Preface

This book provides a summary of several different reinforcement learning (RL) algorithms, including the theory involved in the algorithms as well as coding them using Python and TensorFlow. Specifically, the algorithms covered in this book are Q-learning, SARSA, DQN, DDPG, A3C, TRPO, and PPO. The applications of these RL algorithms include computer games from OpenAI Gym and autonomous driving using the TORCS racing car simulator.

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