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Keras Reinforcement Learning Projects.pdf

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    Title Page
    Copyright and Credits
    Keras Reinforcement Learning Projects
    Packt Upsell
    Why subscribe?
    Packt.com
    Contributors
    About the author
    About the reviewer
    Packt is searching for authors like you
    Preface
    Who this book is for
    What this book covers
    To get the most out of this book
    Download the example code files
    Download the color images
    Conventions used
    Get in touch
    Reviews
    Overview of Keras Reinforcement Learning
    Basic concepts of machine learning
    Discovering the different types of machine learning
    Supervised learning
    Unsupervised learning
    Reinforcement learning
    Building machine learning models step by step
    Getting started with reinforcement learning
    Agent-environment interface
    Markov Decision Process
    Discounted cumulative reward
    Exploration versus exploitation
    Reinforcement learning algorithms
    Dynamic Programming
    Monte Carlo methods
    Temporal difference learning
    SARSA
    Q-learning
    Deep Q-learning
    Summary
    Simulating Random Walks
    Random walks
    One-dimensional random walk
    Simulating 1D random walk
    Markov chains
    Stochastic process
    Probability calculation
    Markov chain definition
    Transition matrix
    Transition diagram
    Weather forecasting with Markov chains
    Generating pseudorandom text with Markov chains
    Summary
    Optimal Portfolio Selection
    Dynamic Programming
    Divide and conquer versus Dynamic Programming
    Memoization
    Dynamic Programming in reinforcement-learning applications
    Optimizing a financial portfolio
    Optimization techniques
    Solving the knapsack problem using Dynamic Programming
    Different approaches to the problem
    Brute force
    Greedy algorithms
    Dynamic Programming
    Summary
    Forecasting Stock Market Prices
    Monte Carlo methods
    Historical background
    Basic concepts of the Monte Carlo simulation
    Monte Carlo applications
    Numerical integration using the Monte Carlo method
    Monte Carlo for prediction and control
    Amazon stock price prediction using Python
    Exploratory analysis
    The Geometric Brownian motion model
    Monte Carlo simulation
    Summary
    Delivery Vehicle Routing Application
    Temporal difference learning
    SARSA
    Q-learning
    Basics of graph theory
    The adjacency matrix
    Adjacency lists
    Graphs as data structures in Python
    Graphs using the NetworkX package
    Finding the shortest path
    The Dijkstra algorithm
    The Dijkstra algorithm using the NetworkX package
    The Google Maps algorithm
    The Vehicle Routing Problem
    Summary
    Continuous Balancing of a Rotating Mechanical System
    Neural network basic concepts
    The Keras neural network model
    Classifying breast cancer using the neural network
    Deep reinforcement learning
    The Keras–RL package
    Continuous control with deep reinforcement learning
    Summary
    Dynamic Modeling of a Segway as an Inverted Pendulum System
    How Segways work
    System modeling basics
    OpenAI Gym
    OpenAI Gym methods
    OpenAI Gym installation
    The CartPole system
    Q-learning solution
    Deep Q-learning solution
    Summary
    Robot Control System Using Deep Reinforcement Learning
    Robot control
    Robotics overview
    Robot evolution
    First-generation robots
    Second-generation robots
    Third-generation robots
    Fourth-generation robots
    Robot autonomy
    Robot mobility
    Automatic control
    Control architectures
    The FrozenLake environment
    The Q-learning solution
    A Deep Q-learning solution
    Summary
    Handwritten Digit Recognizer
    Handwritten digit recognition
    Optical Character Recognition
    Computer vision
    Handwritten digit recognition using an autoencoder
    Loading data
    Model architecture
    Deep autoencoder Q-learning
    Summary
    Playing the Board Game Go
    Game theory
    Basic concepts
    Game types
    Cooperative games
    Symmetrical games
    Zero-sum games
    Sequential games
    Game theory applications
    Prisoner's dilemma
    Stag hunt
    Chicken game
    The Go game
    Basic rules of the game
    Scoring rules
    The AlphaGo project
    The AlphaGo algorithm
    Monte Carlo Tree Search
    Convolutional networks
    Summary
    What's Next?
    Reinforcement-learning applications in real life
    DeepMind AlphaZero
    IBM Watson
    The Unity Machine Learning Agents toolkit
    FANUC industrial robots
    Automated trading systems using reinforcement learning
    Next steps for reinforcement learning
    Inverse reinforcement learning
    Learning by demonstration
    Deep Deterministic Policy Gradients
    Reinforcement learning from human preferences
    Hindsight Experience Replay
    Summary
    Other Books You May Enjoy
    Leave a review - let other readers know what you think
    Keras Reinforcement Learning Projects 9 projects exploring popular reinforcement learning techniques to build self- learning agents Giuseppe Ciaburro
    BIRMINGHAM - MUMBAI
    Keras Reinforcement Learning Projects Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Commissioning Editor: Pravin Dhandre Acquisition Editor: Dayne Castelino Content Development Editor: Karan Thakkar Technical Editor: Nilesh Sawakhande Copy Editor: Safis Editing Project Coordinator: Nidhi Joshi Proofreader: Safis Editing Indexer: Mariammal Chettiyar Graphics: Jisha Chirayil Production Coordinator: Jyoti Chauhan First published: September 2018 Production reference: 1270918 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78934-209-3 www.packtpub.com
    mapt.io Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website.
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