rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Hands-On Q-Learning with Python
  name:
    name part: Habib, Nazia
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2019
    edition: 1st edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (212 pages)
  abstract: Leverage the power of reward-based training for your deep learning models with Python Key Features Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) Study practical deep reinforcement learning using Q-Networks Explore state-based unsupervised learning for machine learning models Book Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym's CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you'll gain insight into what's in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learn Explore the fundamentals of reinforcement learning and the state-action-reward process Understand Markov decision processes Get well-versed with libraries such as Keras, and TensorFlow Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym Choose and optimize a Q-network's learning parameters and fine-tune its performance Discover real-world applications and use cases of Q-learning Who this book is f…
  note: Habib, Nazia.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2019.
  note: Mode of access: World Wide Web.
  identifier: 9781789345803
  identifier: 9781789345759
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 250919
    record change date: 20260128151158.3
    record identifier: in00004137383