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 LearningChapter 2: Theory and AlgorithmsChapter Goal :This Chapter covers the theory of Reinforcement Learning and Algorithms.No of pages : 60Sub-topics1 . Problem scenarios in Reinforcement Learningins2. Markov Decision process3. SARSA4.Q learning5.Value Functions6.Dynamic Programming and Policies7.Approaches to RLChapter 3: Open AI basicsChapter Goal: In this chapter we will cover the basics of Open AI gym and universe andthen move forward for installing it.No of pages: 40Sub - Topics:1. What are Open AI environments2. Installation of Open AI Gym and Universe in Ubuntu3. Difference between Open AI Gym and UniverseChapter 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 python5.Markov Decision process toolbox for Python6.Implementing a Game AI with Reinforcement LearningChapter 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 LearningChapter 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.