Description
Book Synopsis
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 reinfor
Table of Contents
Chapter 1: Reinforcement Learning basicsChapter Goal: This chapter covers the basics needed for AI,ML and Deep Learning.Relation between them and differences.No of pages 30Sub -Topics1. Reinforcement Learning2. The flow3. Faces of Reinforcement Learning4. 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 : 60Sub-topics1 . Problem scenarios in Reinforcement Learningins
2. Markov Decision process3. SARSA4.Q learning5.Value Functions6.Dynamic Programming and Policies7.Approaches to RL
Chapter 3: Open AI basicsChapter 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 wayChapter Goal: We will use Python to start the programming and cover topics accordinglyNo of pages: 60Sub - Topics: 1. Open AI,Open AI Gym and python2. Setting up the environment3. Examples4 Swarm Intelligence using python
5.Markov Decision process toolbox for Python6.Implementing a Game AI with Reinforcement Learning
Chapter 5: Reinforcement learning using Tensor Flow environment and KerasChapter Goal: We cover Reinforcement Learning in terms of Tensorflow and KerasNo of pages: 40Sub - Topics: 1. Tensorflow and Reinforcement Learning2. Q learning with Tensor Flow3. Keras4. Keras and Reinforcement Learning
Chapter 6 Google’s DeepMind and the future of Reinforcement LearningChapter Goal: We cover the descriptions of the above the content.No of pages: 25Sub - Topics: 1. Google’s Deep Mind2. Future of Reinforcement Learning 3. Man VS Machines where is it Heading to.
