Reinforcement Learning

Reinforcement Learning
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Book Description
Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. 

Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI  before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning.
 
The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. 

What You'll Learn 
  • Absorb the core concepts of the reinforcement learning process
  • Use advanced topics of deep learning and AI
  • Work with Open AI Gym, Open AI, and Python 
  • Harness reinforcement learning with TensorFlow and Keras using Python
Who This Book Is For
Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.

Content

Chapter 1: Reinforcement Learning Basics
Chapter 2: RL Theory and Algorithms
Chapter 3: OpenAI Basics
Chapter 4: Applying Python to Reinforcement Learning
Chapter 5: Reinforcement Learning with Keras, TensorFlow, and ChainerRL
Chapter 6: Google’s DeepMind and the Future of Reinforcement Learning

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