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: Model-based reinforcement learning
    sub title: from data to actions with Python-based toolbox
  name:
    name part: Farsi, Milad
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Liu, Jun
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory-optimal control and dynamic programming - or on algorithms-most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework-from design to application-of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, resear…
  note: Milad Farsi, Jun Liu.
  subject:
    topic: Reinforcement learning
  subject:
    topic: Python (Computer program language)
  classification: Q325.6 .F37 2023
  classification: 006.3/1
  identifier: 9781119808589
  identifier: 1119808588
  identifier: 9781119808596
  identifier: 1119808596
  identifier: 9781119808602
  identifier: 111980860X
  identifier: 111980857X
  identifier: 9781119808572
  identifier: 9979000 IEEE
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 221203
    record change date: 20260214214155.0
    record identifier: in00004687842
    language of cataloging:
      language term: eng