Doina discusses the concept of option critic, an algorithm designed to identify subgoals that lead to rewarding states. She highlights how agents initially rely on these abstractions but may discard them when solving single tasks, akin to how experts automate their skills. The conversation opens up intriguing research directions regarding the need for richer environments and alternative methods to enhance agent learning.
