Reinforcement Learning for the Real World

Conceptually reinforcement learning should work well, but in practice it can be problematical.  Like that it is being considered for business applications.  Which are about making decisions.   But you do also have to formulate that decision in context and over time to make it workable.

Reinforcement learning for the real world
Edward Jezierski on the science of bringing creativity and curiosity together in a learning system.
By Jenn Webb

Edward Jezierski interview via O'Reilly

Roger Magoulas recently sat down with Edward Jezierski, reinforcement learning AI principal program manager at Microsoft, to talk about reinforcement learning (RL). They discuss why RL’s role in AI is so important, challenges of applying RL in a business environment, and how to approach ethical and responsible use questions.

Here are some highlights from their conversation:

Reinforcement learning is different than simply trying to detect something in an image or extract something from a data set, Jezierski explains— it’s about making decisions. “That entails a whole set of concepts that are about exploring the unknown,” he says. “You have the notion of exploring versus exploiting, which is do the tried and true versus trying something new. You bring in high-level concepts like the notion of curiosity—how much should you buy as you try new things? The notion of creativity—how crazy are the things you’re willing to try out? Reinforcement learning is a science that studies how these things come together in a learning system. (00:18)   ... "