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What Defines The Core Principles Of Q-Learning?
Dive deep into the foundational concepts of Q-Learning, a powerful reinforcement learning algorithm. This video unravels the core principles that enable intelligent agents to learn optimal actions through trial and error. In this explanation, we cover: Understanding the fundamental concept of Q-values and their role in decision-making. How the ...
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SOLVED: The Q-learning approximation algorithm starts with an initial parameter estimate of 0. As the tabular Q-learning; upon observing 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) + rmaxQ(s', c, 0) Then the parameter theta is simply updated by taking a gradient step with respect to the squared loss L(theta) = sum(y - Q(s, c, theta))^2. The negative gradient can be computed as follows: (Enter y
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