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Books > Business & Economics > Economics > Econometrics
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Control Systems and Reinforcement Learning (Hardcover)
Loot Price: R1,480
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Control Systems and Reinforcement Learning (Hardcover)
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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.
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