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builtin.com
A Step-by-Step Explanation of Stochastic Policy Gradient Algorithms | Built In
In the final installment of this series, we’ll walk through stochastic policy gradients and AI agents in continuous action spaces.
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Insightforge | AI & Data Science on Instagram: "Stochastic Gradient Descent (SGD) is an optimization technique used to reduce a model’s loss and improve its performance. A good way to imagine it is like walking down a hill to reach the lowest point. The gradient tells you the slope and the direction to move downward. But instead of looking at the entire landscape (the full dataset), SGD looks at small random batches of data to estimate the slope at each step. Because it works with smaller sample
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