In the previous lesson, we learned the mathematical definition of a gradient. We saw that the gradient of a function was a combination of our partial derivatives with respect to each variable of that ...

Policy gradient methods have high variance, so it's important to evaluate the reward function over multiple rollouts to find the variance of the agent's accumulated reward. Conveniently, this also ...

This repository hosts Python projects that implement Gradient Descent from scratch for optimizing both 2D and 3D functions. The projects aim to provide a comprehensive understanding of the gradient ...

It demonstrates how to sketch graphs from rules, derive rules from graphs, and calculate key features such as the gradient and \(x\)- and \(y\)- axis intercepts. Features of linear graphs Linear ...

Non-linear optimal control problems often require solution using iterative procedures and, hence, they fall naturally in the realm of 2D systems where the two dimensions are response time horizon and ...

We analyze the problem of reconstructing a 2D function that approximates a set of desired gradients and a data term. The combined data and gradient terms enable operations like modifying the gradients ...

You can imagine a function as a landscape, where the elevation of the land is equal to the value of the function (the “profit”) at that particular spot. Gradient descent searches for the function’s ...