What is a semi gradient?
2 Gradient and Semi-gradient methods. Each of the tabular solution methods has an approximate solution version that use the gradient update rule above. Methods that use this update are therefore called ‘Semi-gradient’ methods, since they ignore part of the gradient.
Does reinforcement learning use Gradient descent?
In addition to improving both the theory and practice of existing types of algorithms, the gradient-descent approach makes it possible to create entirely new classes of reinforcement-learning algorithms. VAPS algorithms can be derived that ignore values altogether, and simply learn good policies directly.
What are the advantages of using gradients instead of residuals?
I have found mentions of two advantages in using gradients instead of actual residuals: 1) Using gradients will allow us to plug in any loss function (not just mse) without having to change our base learners to make them compatible with the loss function.
When was the term gradient boosting first used?
By the way, the term boosting already existed when gradient boosting was invented. It was used for AdaBoost, which changes the weights of the observations based on the loss, rather than training in the residuals. AdaBoost only works for weak models (each model must commit errors), while gradient boosting can do resample and work for strong models.
How is gradient boosting different from neural networks?
One important difference between gradient boosting (discrete optimization) and neural networks (continuous optimization) is that gradient boosting allows you to work with functions whose derivative is constant. In gradient boosting, you can use “weird” functions like MAE or the Pinball function.
Why is the gradient always constant in gradient descent?
The reason why they don’t work well is because the optimization steps use the gradient (in gradient descent), and the gradient is constant for the entire function L ′ ( y, y ^) = 1 or L ′ ( y, y ^) = − 1, so the magnitude of the gradient is always 1.