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2275 results in Pattern Recognition and Machine Learning

Page 33 of 114

  • 0 - Initialization

  • Daniel A. Roberts, Massachusetts Institute of Technology, Sho Yaida
  • With contributions by Boris Hanin
  • Book:
    The Principles of Deep Learning Theory
    Published online:
    05 May 2022
    Print publication:
    26 May 2022, pp 1-10
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Control Systems and Reinforcement Learning
  • Sean Meyn
  • Published online:
    17 May 2022
    Print publication:
    09 June 2022
  • A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

Page 33 of 114