Reinforcement Learning : With Open AI, TensorFlow and Keras Using Python

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. \*\* Contents About the Authors About the Technical Reviewer Acknowledgments Introduction Chapter 1: Reinforcement Learning Basics 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 Deterministic DFA (Deterministic Finite Automata) NDFA (Nondeterministic Finite Automaton) Observable Discrete or Continuous Single Agent and Multiagent Environments Conclusion Chapter 2: RL Theory and Algorithms Theoretical Basis of Reinforcement Learning Where Reinforcement Learning Is Used Manufacturing Inventory Management 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 Conclusion Chapter 3: OpenAI Basics Getting to Know OpenAI Installing OpenAI Gym and OpenAI Universe Working with OpenAI Gym and OpenAI More Simulations OpenAI Universe Conclusion Chapter 4: Applying Python to Reinforcement Learning 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 Ant-Based Routing Crowd Simulations Human Swarming Swarm Grammars Swarmic Art The Rastrigin Function Swarm Intelligence in Python Building a Game AI The Entire TFLearn Code Conclusion Chapter 5: Reinforcement Learning with Keras, TensorFlow, and ChainerRL 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 Chapter 6: Google’s DeepMind and the Future of Reinforcement Learning Google DeepMind Google AlphaGo What Is AlphaGo? Monte Carlo Search Man vs. Machines Positive Aspects of AI Negative Aspects of AI Conclusion Index Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. What You Will Learn Work with the bot development life cycle Master bot UX designIntegrate into the bot ecosystemMaximize the business and monetization potential for bots Who This Book Is For Developers, programmers, and hobbyists who have basic programming knowledge. The book can be used by existing chatbot developers to gain a better understanding of analytics and the business side of bots. 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. You will: 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 Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. You will: Work with the bot development life cycle Master bot UX design Integrate into the bot ecosystem Maximize the business and monetization potential for bots Covering the basics of Reinforcement Learning with the help of the Python programming language, this book touches on several aspects, such as Q learning, MDP, RL with Keras, and OpenAI Gym and OpenAI Environment, and also cover algorithms related to RL. -- Edited summary from book