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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.

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Chapter 1: Reinforcement Learning basics Chapter Goal: This chapter covers the basics needed for AI,ML and Deep Learning.Relation between them and differences. No of pages 30 Sub -Topics 1. Reinforcement Learning 2. The flow 3. Faces of Reinforcement Learning 4. 5. Environments6. The depiction of inter relation between Agents and EnvironmentDeep Learning
Chapter 2: Theory and AlgorithmsChapter Goal :This Chapter covers the theory of Reinforcement Learning and Algorithms. No of pages : 60 Sub-topics 1 . Problem scenarios in Reinforcement Learningins
2. Markov Decision process 3. SARSA 4.Q learning 5.Value Functions 6.Dynamic Programming and Policies 7.Approaches to RL
Chapter 3: Open AI basics Chapter Goal: In this chapter we will cover the basics of Open AI gym and universe and
then move forward for installing it.
No of pages: 40
Sub - Topics:
1. What are Open AI environments
2. Installation of Open AI Gym and Universe in Ubuntu
3. Difference between Open AI Gym and Universe

Chapter 4: Getting to know Open AI and Open AI gym the developers way Chapter Goal: We will use Python to start the programming and cover topics accordingly No of pages: 60 Sub - Topics: 1. Open AI,Open AI Gym and python 2. Setting up the environment 3. Examples 4 Swarm Intelligence using python
5.Markov Decision process toolbox for Python 6.Implementing a Game AI with Reinforcement Learning
Chapter 5: Reinforcement learning using Tensor Flow environment and Keras Chapter Goal: We cover Reinforcement Learning in terms of Tensorflow and Keras N o of pages: 40 Sub - Topics: 1. Tensorflow and Reinforcement Learning 2. Q learning with Tensor Flow 3. Keras 4. Keras and Reinforcement Learning
Chapter 6 Google´s DeepMind and the future of Reinforcement Learning Chapter Goal: We cover the descriptions of the above the content. No of pages: 25 Sub - Topics: 1. Google´s Deep Mind 2. Future of Reinforcement Learning 3. Man VS Machines where is it Heading to.

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