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: Deep Reinforcement Learning with Python
    sub title: With PyTorch, TensorFlow and OpenAI Gym
  name:
    name part: Sanghi, Nimish.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xxu
    date issued: 2021
    edition: 1st ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XIX, 382 pages 132 illustrations)
  abstract: Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise. You'll begin by reviewing the Markov decision processes, Bellman equations, and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and deep learning. This is followed by various deep reinforcement learning algorithms such as deep q-networks, various flavors of actor-critic methods, and other policy-based methods. You'll also look at exploration vs exploitation dilemma, a key consideration in reinforcement learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation using popular deep learning frameworks such as TensorFlow and PyTorch. In the end, you'll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym. You will: Examine deep reinforcement learning Implement deep learning algorithms using OpenAI's Gym environment Code your own game playing agents for Atari using actor-critic algorithms Apply best practices for model building and algorithm training .
  table of contents: Chapter 1: Introduction to Deep Reinforcement Learning -- Chapter 2: Markov Decision Processes -- Chapter 3: Model Based Algorithms -- Chapter 4: Model Free Approaches -- Chapter 5: Function Approximation -- Chapter 6:Deep Q-Learning -- Chapter 7: Policy Gradient Algorithms -- Chapter 8: Combining Policy Gradients and Q-Learning -- Chapter 9: Integrated Learning and Planning -- Chapter 10: Further Exploration and Next Steps.
  note: by Nimish Sanghi.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Python
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer Nature eBook
  identifier: 9781484268094
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 210401
    record change date: 20230330185419.1
    record identifier: in00004420679