Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

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Packt Publishing Ltd, Apr 24, 2018 - Computers - 334 pages
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Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow

Key Features
  • Learn reinforcement learning concepts and their implementation using TensorFlow
  • Discover different problem-solving methods for Reinforcement Learning
  • Apply reinforcement learning for autonomous driving cars, robobrokers, and more
Book Description

Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions.

The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.

By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.

What you will learn
  • Implement state-of-the-art Reinforcement Learning algorithms from the basics
  • Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more
  • Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP
  • Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym
  • Understand how Reinforcement Learning Applications are used in robotics
Who this book is for

If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.

 

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Contents

Preface
1
Deep Learning Architectures and Frameworks
7
Training Reinforcement Learning Agents Using OpenAI Gym
62
Markov Decision Process
78
Policy Gradients
101
QLearning and Deep QNetworks
125
Asynchronous Methods
175
Robo Everything Real Strategy Gaming
192
Financial Portfolio Management
235
Reinforcement Learning in Robotics
247
Deep Reinforcement Learning in Ad Tech
261
Reinforcement Learning in Image Processing
269
Deep Reinforcement Learning in NLP
281
Further topics in Reinforcement Learning
297
Other Books You May Enjoy
307
Index
310

AlphaGo Reinforcement Learning at Its Best
201
Reinforcement Learning in Autonomous Driving
219

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About the author (2018)

Sayon Dutta is an Artificial Intelligence researcher and developer. A graduate from IIT Kharagpur, he owns the software copyright for Mobile Irrigation Scheduler. At present, he is an AI engineer at Wissen Technology. He co-founded an AI startup Marax AI Inc., focused on AI-powered customer churn prediction. With over 2.5 years of experience in AI, he invests most of his time implementing AI research papers for industrial use cases, and weightlifting.

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