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The Q-learning approximation algorithm starts with an initial parameter estimate of 0. As the tabular Q-learning, upon observing a data tuple (s, c, R (s, c), s'), the target value y for the Q-value of (s, c) is defined as the sampled version of the Bellman operator, y= R(s,c) + ym )+7max Q (s', c', 0).

The Q-learning approximation algorithm starts with an initial parameter estimate of 0. As the tabular Q-learning, upon observing a data tuple (s, c, R (s, c), s'), the target value y for the Q-value of (s, c) is defined as the sampled version of the Bellman operator, y= R(s,c) + ym )+7max Q (s', c', 0).

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Computing theta update rule possible (graded) The Q-learning approximation algorithm starts with an initial parameter estimate of 0. As the tabular Q-learning, upon observing a data tuple (s, c, R (s, c), s'), the target value y for the Q-value of (s, c) is defined as the sampled version of the Bellman operator, Then the parameter is simply updated by taking a gradient step with respect to the squared loss y = = R(s, c) + ymax Q (s', c', 0). L(0) = The negative gradient can be computed as follows: (Enter your answer in terms of y, Q(s, c, theta), and phi(s, c).). g (0) = = L (0) = 1/2 (y — Q (s, c, 0))².
Transcribed Image Text:Computing theta update rule possible (graded) The Q-learning approximation algorithm starts with an initial parameter estimate of 0. As the tabular Q-learning, upon observing a data tuple (s, c, R (s, c), s'), the target value y for the Q-value of (s, c) is defined as the sampled version of the Bellman operator, Then the parameter is simply updated by taking a gradient step with respect to the squared loss y = = R(s, c) + ymax Q (s', c', 0). L(0) = The negative gradient can be computed as follows: (Enter your answer in terms of y, Q(s, c, theta), and phi(s, c).). g (0) = = L (0) = 1/2 (y — Q (s, c, 0))².
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