Chapter 9: Recent Advancements and Next Steps

Different types of inequality measures are Gini coefficients, the Theil index, and the variance of algorithms.
The Theil index is the most commonly used inequality measure. It's named after a Dutch econometrician, Henri Theil, and it's a special case of the family of inequality measures called generalized entropy measures. It can be defined as the difference between the maximum entropy and observed entropy.
If we enable our robot to learn by just looking at our actions, then we can easily make the robot learn complex goals efficiently and we don't have to engineer complex goal and reward functions. This type of learning—that is, learning from human actions—is called imitation learning, where the robot tries to mimic human action.
A concept generator is used to extract features. We can use deep neural nets that are parameterized by some parameter, , to generate the concepts. For examples, our concept generator can be a CNN if our input is an image.
We sample a batch of tasks from the task distributions, learn their concepts via the concept generator, perform meta learning on those concepts, and then we compute the meta learning loss:

Table of Contents for Chapter 9: Recent Advancements and Next Steps