The article on Reinforcement Learning Free PDF Download provides you with the way how to start learning Reinforcement as a beginner.
This article helps a lot to understand basic to advanced Reinforcement Learning in a detailed form.
Let’s start the explanation of each Chapter in detail on every topic related to Reinforcement Learning.
Chapter 1: Reinforcement Learning Basics Here, you grab the knowledge about the basic of Reinforcement Learning and what is Reinforcement learning.
The topics that come under Chapter 1- What Is Reinforcement Learning, Faces of Reinforcement Learning, The Flow of Reinforcement Learning, Different Terms in Reinforcement Learning, Gamma, Lambda, Interactions with Reinforcement Learning, RL Characteristics, How Reward Works, Agents, RL Environments, and Conclusion.
Chapter 2: RL Theory and AlgorithmsComplete information on RL theory and its algorithm. The working process of it.
Some of the topics covered in Chapter 2 are- Theoretical Basis of Reinforcement Learning, Where Reinforcement Learning Is Used, Manufacturing, Inventory Management, Contents, Delivery Management, Finance Sector, Why Is Reinforcement Learning Difficult, Preparing the Machine, Installing Docker, An Example of Reinforcement Learning with Python, What Are Hyperparameters, Writing the Code, What Is MDP, The Markov Property, The Markov Chain, MDPs, SARSA, Temporal Difference Learning, How SARSA Works, Q Learning, What Is Q, How to Use Q, SARSA Implementation in Python, The Entire Reinforcement Logic in Python, Dynamic Programming in Reinforcement Learning, and Conclusion.
Chapter 3: OpenAI Basics It explains the relationship of AI with Reinforcement Learning and its uses of it.
The topics covered in Chapter 3- Getting to Know OpenAI, Installing OpenAI Gym and OpenAI Universe, Working with OpenAI Gym and OpenAI, More Simulations, Contents, OpenAI Universe, and Conclusion.
Chapter 4: Applying Python to Reinforcement Learning Here, you get an idea about how Python applies reinforcement Learning.
Some topics that come under Chapter 4- Q Learning with Python, The Maze Environment Python File, The RL_Brain Python File, Updating the Function, Using the MDP Toolbox in Python, Understanding Swarm Intelligence, Applications of Swarm Intelligence, Swarm Grammars, The Rastrigin Function, Swarm Intelligence in Python, Building a Game AI, The Entire TFLearn Code, and Conclusion.
Chapter 5: Reinforcement Learning with Keras, TensorFlow, and ChainerRL In this Chapter, you can learn how Keras can be used for Deep Learning.
Some of the topics of Chapter 5- What Is Keras, Using Keras for Reinforcement Learning Using ChainerRL, Installing ChainerRL, Pipeline for Using ChainerRL, Deep Q Learning: Using Keras and TensorFlow, Installing Keras-rl, Training with Keras-rl Conclusion, and Contents.
Chapter 6: Google’s DeepMind and the Future of Reinforcement Learning Here, you can see what happens between man and computer and get a deep knowledge of Google DeepMind and Google AlphaGo.
The topics that come under Chapter 6 are- Google DeepMind, Google AlphaGo, What Is AlphaGo, Monte Carlo Search, Man vs. Machines, Positive Aspects of AI, Negative Aspects of AI, and Conclusion.
Conclusion- The article on Reinforcement Learning is helpful for beginners who don’t know anything about it. They get the idea to start to study it as a starter. For information related to the topic then visit the pdf of it.
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